diff --git a/docs/source/explainable_categorical_alternate.ipynb b/docs/source/explainable_categorical_alternate.ipynb index 6896e229..52f944e3 100644 --- a/docs/source/explainable_categorical_alternate.ipynb +++ b/docs/source/explainable_categorical_alternate.ipynb @@ -11,7 +11,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The **Explainable Reasoning with ChiRho** package aims to provide a systematic, unified approach to causal explanation computations. The package provides a single generic program transformation that can be applied to any arbitrary causal model representable as a Chirho program. This program transformation allows several causal explanation queries to be modeled in terms of probabilistic queries. This approach of reducing causal queries to probabilistic computations on transformed causal models is the foundational idea behind all of ChiRho and has been leveraged for causal explanations in this module as well.\n", + "The **Explainable Reasoning with ChiRho** package aims to provide a systematic, unified approach to causal explanation computations. The package provides a single generic program transformation that can be applied to any arbitrary causal model representable as a Chirho program. This program transformation allows several causal explanation queries to be modeled in terms of probabilistic queries. This approach of reducing causal queries to probabilistic computations on transformed causal models is the foundational idea behind all of ChiRho and in this module, has been leveraged for causal explanations as well.\n", "\n", "The goal of this notebook is to illustrate how the package can be used to provide an approximate method of answering a range of causal explanation queries in causal models with only categorical variables. As the key tool will involve sampling-based posterior probability estimation, a lot of what will be said *mutatis mutandis* applies to more general settings where variables are continuous (to which we will devote another tutorial).\n", "\n", @@ -24,14 +24,15 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "[Poorva: come back to this outline for better headlines]\n", - "\n", "**Outline**\n", "\n", - "[Causal explanation and counterfactual thinking](#causal-explanation-and-counterfactual-thinking) \n", + "[Overview](#overview)\n", + "\n", + "[Setup](#setup)\n", "\n", + "[But-for Causal Explanations](#but-for-causal-explanations) \n", "\n", - "[Witness nodes and context sensitivity](#witness-nodes-and-context-sensitivity)\n", + "[Context-sensitive Causal Explanations](#witness-nodes-and-context-sensitivity)\n", "\n", "[Probability of causation and responsibility](#probability-of-causation-and-responsibility)" ] @@ -40,7 +41,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Causal explanation and counterfactual thinking" + "## Overview" ] }, { @@ -76,12 +77,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Setup" + "## Setup" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -120,7 +121,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -147,7 +148,7 @@ " *args,\n", " num_samples=num_samples,\n", " max_plate_nesting=max_plate_nesting,\n", - " normalized=True,\n", + " normalized=False,\n", " **kwargs\n", " )\n", "\n", @@ -162,6 +163,13 @@ " return {i: KernelSoftConditionReparam(_soft_eq) for i in data}" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## But-for Causal Explanations" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -171,9 +179,16 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 20, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'u_match_dropped': Boolean(), 'match_dropped': IndependentConstraint(Real(), 0), 'u_lightning': Boolean(), 'lightning': IndependentConstraint(Real(), 0), 'smile': Boolean(), 'forest_fire': IndependentConstraint(Real(), 0)}\n" + ] + }, { "data": { "image/svg+xml": [ @@ -227,10 +242,10 @@ "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 64, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -283,7 +298,9 @@ "4. the elements of the current context (`witnesses`), and \n", "5. the `consequents` of interest $Y=y$. \n", "\n", - "The `SearchForExplanation` handler then takes these arguments and transforms the original model into another model in which interventions on antecedents and witnesses are applied stochastically. Once the antecedents `A` and witnesses `W` are chosen, parallel counterfactual worlds are created to condition on `A` being sufficient and necessary causes for the consequent with the context `W`." + "The `SearchForExplanation` handler then takes these arguments and transforms the original model into another model in which interventions on antecedents and witnesses are applied stochastically. Once the antecedents `A` and witnesses `W` are chosen, parallel counterfactual worlds are created to condition on `A` being sufficient and necessary causes for the consequent with the context `W`.\n", + "\n", + "And now, we are ready to use `SearchForExplanation` for answering but-for causal questions." ] }, { @@ -299,14 +316,14 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.2951)\n" + "tensor(0.2947)\n" ] } ], @@ -333,14 +350,14 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.5970)\n" + "tensor(0.5993)\n" ] } ], @@ -353,35 +370,33 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "A similar estimation as above would not work in case of overdetermination where one of the two factors are enough to cause the outcome. Consider the case where both `match_dropped` and `lightning` did occur. In this case, if we try to determine the causal role of `match_dropped`, it would come out to be zero. And a symmetric reasoning works for lightning as well. This results in $P(f'_{m'}, f_m, m, l) = P(f'_{l'}, f_l, m, l)=0$" + "**Causal Query 2** Is a Chirho developer smiling a cause of forest fire?\n", + "\n", + "The intuitive answer is obviously no and we show that the same conclusion can be drawn using `SearchForExplanation` handler." ] }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(2.8615e-06)\n" + "tensor(1.0011e-05)\n" ] } ], "source": [ "query = SearchForExplanation(\n", " supports=forest_fire_supports,\n", - " antecedents={\"match_dropped\": torch.tensor(1.0)},\n", + " antecedents={\"smile\": torch.tensor(1.0)},\n", " consequents={\"forest_fire\": torch.tensor(1.0)},\n", " witnesses={}, # potential context elements, we leave them empty for now\n", - " alternatives={\"match_dropped\": torch.tensor(0.0)},\n", + " alternatives={\"smile\": torch.tensor(0.0)},\n", " consequent_scale=1e-5,\n", - ")(\n", - " pyro.poutine.reparam(config=reparam_config([\"match_dropped\", \"lightning\"]))(\n", - " condition(data={\"match_dropped\": torch.tensor(1.0), \"lightning\": torch.tensor(1.0)})\n", - " (forest_fire_model)\n", - " ))\n", + ")(forest_fire_model)\n", "\n", "logp, trace, mwc, log_weights = importance_infer(num_samples=10000)(query)()\n", "print(torch.exp(logp))" @@ -389,19 +404,19 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(2.8286e-06)\n" + "tensor(1.0011e-05)\n" ] } ], "source": [ - "mask_intervened = trace.nodes[\"__cause____antecedent_match_dropped\"][\"value\"] == 0\n", + "mask_intervened = trace.nodes[\"__cause____antecedent_smile\"][\"value\"] == 0\n", "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze())/torch.sum(mask_intervened.float()))" ] }, @@ -409,29 +424,29 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "But if we consider both `match_dropped` and `lightning` to be possible causes, we can estimate $P(f'_{m',l'}, f_{m,l}, m, l)$ to determine their causal role." + "A similar estimation as above would not work in case of overdetermination where one of the two factors are enough to cause the outcome. Consider the case where both `match_dropped` and `lightning` did occur. In this case, if we try to determine the causal role of `match_dropped`, it would come out to be zero. And a symmetric reasoning works for lightning as well. This results in $P(f'_{m'}, f_m, m, l) = P(f'_{l'}, f_l, m, l)=0$. This is a canonical example that shows the limitations of but-for analysis." ] }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.0692)\n" + "tensor(2.8025e-06)\n" ] } ], "source": [ "query = SearchForExplanation(\n", " supports=forest_fire_supports,\n", - " antecedents={\"match_dropped\": torch.tensor(1.0), \"lightning\": torch.tensor(1.0)},\n", + " antecedents={\"match_dropped\": torch.tensor(1.0)},\n", " consequents={\"forest_fire\": torch.tensor(1.0)},\n", " witnesses={}, # potential context elements, we leave them empty for now\n", - " alternatives={\"match_dropped\": torch.tensor(0.0), \"lightning\": torch.tensor(0.0)},\n", + " alternatives={\"match_dropped\": torch.tensor(0.0)},\n", " consequent_scale=1e-5,\n", ")(\n", " pyro.poutine.reparam(config=reparam_config([\"match_dropped\", \"lightning\"]))(\n", @@ -445,19 +460,19 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.2796)\n" + "tensor(2.7553e-06)\n" ] } ], "source": [ - "mask_intervened = (trace.nodes[\"__cause____antecedent_match_dropped\"][\"value\"] == 0) & (trace.nodes[\"__cause____antecedent_lightning\"][\"value\"] == 0)\n", + "mask_intervened = trace.nodes[\"__cause____antecedent_match_dropped\"][\"value\"] == 0\n", "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze())/torch.sum(mask_intervened.float()))" ] }, @@ -465,47 +480,70 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "DONE TILL HERE" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Witness nodes and context sensitivity" + "But if we consider both `match_dropped` and `lightning` to be possible causes, we can estimate $P(f'_{m',l'}, f_{m,l}, m, l)$ to determine their causal role that comes out to be greater than 0." ] }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.4616)\n" + "tensor(0.0682)\n" ] } ], "source": [ "query = SearchForExplanation(\n", " supports=forest_fire_supports,\n", - " antecedents={\"match_dropped\": 1.0, \"lightning\": 1.0},\n", + " antecedents={\"match_dropped\": torch.tensor(1.0), \"lightning\": torch.tensor(1.0)},\n", " consequents={\"forest_fire\": torch.tensor(1.0)},\n", - " witnesses={},\n", - " alternatives={\"match_dropped\": 0.0, \"lightning\": 0.0},\n", - ")(forest_fire_model)\n", + " witnesses={}, # potential context elements, we leave them empty for now\n", + " alternatives={\"match_dropped\": torch.tensor(0.0), \"lightning\": torch.tensor(0.0)},\n", + " consequent_scale=1e-5,\n", + ")(\n", + " pyro.poutine.reparam(config=reparam_config([\"match_dropped\", \"lightning\"]))(\n", + " condition(data={\"match_dropped\": torch.tensor(1.0), \"lightning\": torch.tensor(1.0)})\n", + " (forest_fire_model)\n", + " ))\n", "\n", - "logp, trace, mwc = importance_infer(num_samples=10000)(query)()\n", + "logp, trace, mwc, log_weights = importance_infer(num_samples=10000)(query)()\n", "print(torch.exp(logp))" ] }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(0.2793)\n" + ] + } + ], + "source": [ + "mask_intervened = (trace.nodes[\"__cause____antecedent_match_dropped\"][\"value\"] == 0) & (trace.nodes[\"__cause____antecedent_lightning\"][\"value\"] == 0)\n", + "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze())/torch.sum(mask_intervened.float()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Context-sensitive Causal Explanations" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ - "This already suggests a more complicated picture, as it turns out that we need to pay attention to membership in larger antecedent sets that would make a difference (that is one reason why we need stochasticity in antecedent candidate preemption: to search for such sets).\n", + "As the previous example showed, but-for analysis is not sufficient for our analysis. It induces the need to pay attention to membership of variables in larger antecedent sets that would make a difference (that is one reason why we need stochasticity in antecedent candidate preemption: to search for such sets).\n", "\n", "But even then, the but-for analysis does not pay sufficient attention to the granularity of a given problem and its causal structure. There are asymmetric cases where the efficiency of one cause prevents the efficiency of another, in which our causal attributions should also be asymmetric, but \"being a member of the same larger antecedent set\" isn't.\n", "\n", @@ -514,7 +552,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -597,7 +635,7 @@ "bill_hits\n", "\n", "\n", - "\n", + "\n", "prob_bill_hits->bill_hits\n", "\n", "\n", @@ -615,7 +653,7 @@ "bottle_shatters\n", "\n", "\n", - "\n", + "\n", "prob_bottle_shatters_if_sally->bottle_shatters\n", "\n", "\n", @@ -627,7 +665,7 @@ "prob_bottle_shatters_if_bill\n", "\n", "\n", - "\n", + "\n", "prob_bottle_shatters_if_bill->bottle_shatters\n", "\n", "\n", @@ -645,13 +683,13 @@ "\n", "\n", "\n", - "\n", + "\n", "sally_hits->bill_hits\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "sally_hits->bottle_shatters\n", "\n", "\n", @@ -666,10 +704,10 @@ "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 3, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -747,7 +785,7 @@ }, { "cell_type": "code", - "execution_count": 186, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -777,7 +815,7 @@ " }\n", ")(stones_model))\n", "\n", - "logp, trace, mwc = importance_infer(num_samples=10000)(query)()\n", + "logp, trace, mwc, log_weights = importance_infer(num_samples=10000)(query)()\n", "print(torch.exp(logp))" ] }, @@ -792,7 +830,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "An intuitive solution to the problem, inspired by the Pearl-Halpern definition of actual causality (which we discuss in [another notebook](https://basisresearch.github.io/chirho/actual_causality.html)) is to say that **in answering actual causality queries, we need to consider what happens when part of the actual context is kept fixed.** For instance, in the bottle shattering example, given the observed fact that Bob’s stone didn’t hit, in the counterfactual world in which we keep this observed fact fixed, if Sally nad not thrown the stone, the bottle in fact would not have shattered. \n", + "An intuitive solution to the problem, inspired by the Pearl-Halpern definition of actual causality (which we discuss in [another notebook](https://basisresearch.github.io/chirho/actual_causality.html)) is to say that **in answering actual causality queries, we need to consider what happens when part of the actual context is kept fixed.** For instance, in the bottle shattering example, given the observed fact that Bob’s stone didn’t hit, in the counterfactual world in which we keep this observed fact fixed, if Sally had not thrown the stone, the bottle in fact would not have shattered. \n", "\n", "\n", "For this reason, our handler allows not only stochastic preemption of interventions (to approximate the search through possible antecedent sets) but also stochastic witness preemption of those nodes that are considered part of the context (these needn't exclude each other). In a witness preemption, we ensure that the counterfactual value is identical to the factual one (and by applying it randomly to candidate witness nodes, we approximate a search through all possible context sets)." @@ -800,16 +838,14 @@ }, { "cell_type": "code", - "execution_count": 292, + "execution_count": 47, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor([-1.1512e+01, -1.1512e+01, -1.1512e+01, ..., -2.3024e+01,\n", - " -1.1512e+01, -2.0027e-05])\n", - "tensor(0.2495)\n" + "tensor(0.2513)\n" ] } ], @@ -832,46 +868,17 @@ " }\n", ")(stones_model))\n", "\n", - "logp, trace, mwc, logw = importance_infer(num_samples=100000)(query)()\n", + "logp, trace, mwc, log_weights = importance_infer(num_samples=100000)(query)()\n", "print(torch.exp(logp))" ] }, - { - "cell_type": "code", - "execution_count": 151, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor(1.)\n", - "torch.Size([1, 1, 1, 1, 1, 1, 1, 1, 1, 1])\n", - "torch.Size([1, 1, 1, 1, 1, 3, 1, 1, 1, 1])\n", - "torch.Size([1, 1, 1, 1, 1, 1, 1, 1, 1, 1])\n", - "tensor([1., 0., 1.])\n", - "tensor([ 0.0000, -0.0101, -0.0101])\n" - ] - } - ], - "source": [ - "trace.nodes.keys()\n", - "print(trace.nodes['sally_throws'][\"value\"].squeeze())\n", - "print(trace.nodes['__cause____antecedent_sally_throws'][\"value\"].shape)\n", - "print(trace.nodes['bill_hits'][\"value\"].shape)\n", - "print(trace.nodes['__cause____witness_bill_hits'][\"value\"].shape)\n", - "print(trace.nodes['bottle_shatters'][\"value\"].squeeze())\n", - "print(trace.nodes['__cause____consequent_bottle_shatters'][\"log_prob\"].squeeze())" - ] - }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Admittedly, our search through contexts is very simple and degenerate, as the only part of the actual context which stochastically is kept fixed at the factual value is `bill_hits`. But already with this search, sally throwing is diagnosed as having non-null probability. In fact, the definition of actual causality in Halpern's book (*Actual causality*) contains an existential quantifier: a variable is an actual cause if there is at least one context in which a change in the outcome variable would result from changing the antecedent to have an alternative value, so our search provides a correct diagnosis here.\n", + "Admittedly, our search through contexts is simple as the only part of the actual context which stochastically is kept fixed at the factual value is `bill_hits`. But already with this search, sally throwing is diagnosed as having non-null probability. In fact, the definition of actual causality in Halpern's book (*Actual causality*) contains an existential quantifier: a variable is an actual cause if there is at least one context in which a change in the outcome variable would result from changing the antecedent to have an alternative value, so our search provides a correct diagnosis here.\n", "\n", - "Crucally, as intended, an analogous inference for whether `bill_throws` is a cause of the bottle shattering, yields a different\n", - "result and assigns null causal role to bill." + "Crucially, as intended, an analogous inference for whether `bill_throws` is a cause of the bottle shattering, yields a different result and assigns null causal role to bill." ] }, { @@ -914,7 +921,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Probability of causation and responsibility" + "## Probability of Causation and Responsibility" ] }, { @@ -930,213 +937,14 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 49, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - 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" -2.0385e-05, -2.3024e+01, -2.3024e+01, -2.3024e+01, -2.3024e+01,\n", - " -2.3024e+01, -2.0385e-05, -2.3024e+01, -2.3024e+01, -2.3024e+01,\n", - " -2.0385e-05, -2.3024e+01, -2.0385e-05, -2.3024e+01, -2.3024e+01,\n", - " -2.3024e+01, -2.3024e+01, -2.0385e-05, -2.0385e-05, -2.3024e+01,\n", - " -2.3024e+01, -2.3024e+01, -2.0385e-05, -2.3024e+01, -2.3024e+01,\n", - " -2.3024e+01, -2.3024e+01, -2.3024e+01, -2.3024e+01, -2.0385e-05,\n", - " -2.3024e+01, -2.3024e+01, -2.0385e-05, -2.3024e+01, -2.0385e-05,\n", - " -2.3024e+01, -2.3024e+01, -2.3024e+01, -2.0385e-05, -2.3024e+01,\n", - " -2.3024e+01, -2.0385e-05, -2.0385e-05, -2.3024e+01, -2.3024e+01,\n", - " -2.3024e+01, -2.3024e+01, -2.0385e-05, -2.3024e+01, -2.3024e+01,\n", - " -2.0385e-05, -2.0385e-05, -2.3024e+01, -2.3024e+01, -2.0385e-05,\n", - " -2.3024e+01, -2.3024e+01, -2.3024e+01, -2.3024e+01, -2.3024e+01,\n", - " -2.0385e-05, -2.3024e+01, -2.3024e+01, -2.3024e+01, -2.0385e-05,\n", - " -2.3024e+01, -2.3024e+01, -2.3024e+01, -2.3024e+01, -2.3024e+01,\n", - " -2.3024e+01, -2.3024e+01, -2.3024e+01, -2.3024e+01, -2.3024e+01,\n", - " -2.3024e+01, -2.0385e-05, -2.0385e-05, -2.3024e+01, -2.3024e+01,\n", - " -2.3024e+01, -2.3024e+01, -2.0385e-05, -2.3024e+01, -2.3024e+01,\n", - " -2.3024e+01, -2.3024e+01, -2.3024e+01, -2.3024e+01, -2.3024e+01])\n" + "tensor(0.1439)\n" ] } ], @@ -1150,9755 +958,126 @@ " consequent_scale=1e-5\n", ")(condition(\n", " data={\n", - " \"prob_sally_throws\": torch.tensor(1.0),\n", - " \"prob_bill_throws\": torch.tensor(0.0),\n", - " \"prob_sally_hits\": torch.tensor(1.0),\n", - " \"prob_bill_hits\": torch.tensor(0.0),\n", - " \"prob_bottle_shatters_if_sally\": torch.tensor(1.0),\n", - " \"prob_bottle_shatters_if_bill\": torch.tensor(0.0),\n", + " \"prob_sally_throws\": torch.tensor(0.8),\n", + " \"prob_bill_throws\": torch.tensor(0.7),\n", + " \"prob_sally_hits\": torch.tensor(0.9),\n", + " \"prob_bill_hits\": torch.tensor(0.8),\n", + " \"prob_bottle_shatters_if_sally\": torch.tensor(0.9),\n", + " \"prob_bottle_shatters_if_bill\": torch.tensor(0.8),\n", " \"bottle_shatters\": torch.tensor(1.0),\n", " }\n", ")(stones_model))\n", "\n", - "logp, trace, mwc, logw = importance_infer(num_samples=1000)(query)()\n", - "\n", - "# print(logp)\n", - "\n", - "# print(torch.exp(logp))\n", - "\n", - "# nodes = trace.nodes[\"_RETURN\"][\"value\"]\n", - "\n", - "# print(trace.nodes[\"sally_throws\"][\"value\"].shape)\n", - "# print(nodes[\"sally_throws\"].shape)\n", - "# with mwc:\n", - "# print(gather(nodes[\"sally_throws\"], IndexSet(sally_throws={0})).squeeze())\n", - "# print(gather(nodes[\"sally_throws\"], IndexSet(sally_throws={1})).squeeze())\n", - "# print(gather(nodes[\"sally_throws\"], IndexSet(sally_throws={2})).squeeze())\n", - "\n", - "# print(gather(nodes[\"bill_throws\"], IndexSet(bill_throws={0})).squeeze())\n", - "# print(gather(nodes[\"bill_throws\"], IndexSet(bill_throws={1})).squeeze())\n", - "# print(gather(nodes[\"bill_throws\"], IndexSet(bill_throws={2})).squeeze())\n", - "\n", - "# st_responsible = gather(nodes[\"sally_throws\"], IndexSet(sally_throws={1})) != \\\n", - "# gather(nodes[\"sally_throws\"], IndexSet(sally_throws={2}))\n", - "\n", - "# bt_responsible = gather(nodes[\"bill_throws\"], IndexSet(bill_throws={1})) != \\\n", - "# gather(nodes[\"bill_throws\"], IndexSet(bill_throws={2}))\n", - "\n", - "# print(\"Degree of responsibility of Sally:\", st_responsible.sum() / st_responsible.numel())\n", - "# print(\"Degree of responsibility of Billy:\", bt_responsible.sum() / bt_responsible.numel())" + "logp, trace, mwc, log_weights = importance_infer(num_samples=1000)(query)()\n", + "print(torch.exp(logp))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Note that we assumed Sally to be more likely to throw, more likely to hit, and more likely to shatter the bottle if she hits. For this reason, we expect her to be more likely to be causally responsible for the outcome. Conceptually, these estimates are impacted by some hyperparameters, such as witness preemption probabilities, so perhaps a bit more clarity on can be gained if we think we have a complete list of potential causes and normalize. " + "Now we show how our earlier analysis on the `stones_model` can be carried out through some analysis on the samples we get through this model where we have both `sally_throw` and `bill_throws` as candidate causes and both `bill_hits` and `sally_hits` as context nodes." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We first compute the probability of causation for `sally_throws`. We compute the probability that the set {sally_throws=1} is the cause of bottle shattering." ] }, { "cell_type": "code", - "execution_count": 298, + "execution_count": 50, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "tensor(0.8100)" - ] - }, - "execution_count": 298, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(0.1956)\n" + ] } ], "source": [ - "st_responsible2 = st_responsible.float()\n", - "st_responsible2[st_responsible2 == 0.0] = 9.2\n", - "st_responsible2[st_responsible2 == 1.0] = 1.0\n", - "# st_responsible2\n", - "logp = torch.logsumexp(st_responsible2.squeeze().float() * logw, dim=0) - torch.log(torch.tensor(1000))\n", - "torch.exp(logp)" + "mask_intervened = (trace.nodes[\"__cause____antecedent_sally_throws\"][\"value\"] == 0) & (trace.nodes[\"__cause____antecedent_bill_throws\"][\"value\"] == 1)\n", + "print(torch.sum(torch.exp(log_weights) * mask_intervened.float().squeeze())/mask_intervened.float().sum())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We similarly compute this probability for `bill_throws`." ] }, { "cell_type": "code", - "execution_count": 261, + "execution_count": 51, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "tensor([False, False, True, True, True, True, True, False, True, False,\n", - " True, True, False, True, True, False, True, True, True, True,\n", - " True, True, False, True, True, True, True, True, True, True,\n", - " False, True, True, False, False, True, True, True, True, True,\n", - " True, True, True, True, True, True, True, True, True, False,\n", - " True, False, True, True, True, True, False, True, True, False,\n", - " False, False, True, True, True, True, True, False, True, True,\n", - " False, True, True, True, True, False, True, True, True, True,\n", - " True, True, True, False, True, True, False, True, False, True,\n", - " True, True, False, True, False, True, True, True, True, True,\n", - " True, False, True, False, True, False, False, True, True, True,\n", - " True, False, True, True, True, True, True, False, False, True,\n", - " False, True, True, True, True, True, True, True, True, True,\n", - 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" True, True, True, False, True, True, True, True, True, True,\n", - " True, True, False, False, True, True, True, True, True, True,\n", - " True, True, True, True, False, True, True, True, True, False,\n", - " True, True, True, True, True, True, True, True, True, False,\n", - " True, True, True, True, False, False, True, True, True, False,\n", - " True, True, True, False, False, True, False, True, False, True,\n", - " True, True, True, True, True, True, True, False, True, False,\n", - " True, True, True, True, True, True, True, True, True, False,\n", - " False, True, True, False, True, True, True, True, True, True,\n", - " True, True, True, True, True, False, True, True, False, True,\n", - " False, False, True, True, True, True, False, True, True, False,\n", - " False, True, True, True, True, False, True, False, True, False])" - ] - }, - "execution_count": 261, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(0.0567)\n" + ] } ], "source": [ - "st_responsible.squeeze()" + "mask_intervened = (trace.nodes[\"__cause____antecedent_sally_throws\"][\"value\"] == 1) & (trace.nodes[\"__cause____antecedent_bill_throws\"][\"value\"] == 0)\n", + "print(torch.sum(torch.exp(log_weights) * mask_intervened.float().squeeze())/mask_intervened.float().sum())" ] }, { - "cell_type": "code", - "execution_count": 60, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "query = SearchForExplanation(\n", - " supports=stones_supports,\n", - " antecedents={\"sally_throws\": torch.tensor(1.0), \"bill_throws\": torch.tensor(1.0)},\n", - " consequents={\"bottle_shatters\": torch.tensor(1.0)},\n", - " witnesses={\"sally_hits\": torch.tensor(0.0)},\n", - " alternatives={\"sally_throws\": torch.tensor(0.0), \"bill_throws\": torch.tensor(0.0)},\n", - " antecedent_bias=-0.5,\n", - " witness_bias=0.5,\n", - " consequent_scale=1e-5\n", - ")(condition(\n", - " data={\n", - " \"prob_sally_throws\": torch.tensor(1.0),\n", - " \"prob_bill_throws\": torch.tensor(0.0),\n", - " \"prob_sally_hits\": torch.tensor(1.0),\n", - " \"prob_bill_hits\": torch.tensor(0.0),\n", - " \"prob_bottle_shatters_if_sally\": torch.tensor(1.0),\n", - " \"prob_bottle_shatters_if_bill\": torch.tensor(1.0),\n", - " \"bottle_shatters\": torch.tensor(1.0),\n", - " }\n", - ")(stones_model))" + "We can also use the same model as above to compute the degree of responsibility for bill and sally as follows. We interpret the degree of responsbility asisgned to sally for bottle shattering as the probability that `sally_throws=1` is part of the cause." ] }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 52, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor([[[[[[[[[[False]]]]]]]]],\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - " [[[[[[[[[ True]]]]]]]]],\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - " [[[[[[[[[ True]]]]]]]]],\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - " [[[[[[[[[False]]]]]]]]],\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - " [[[[[[[[[ True]]]]]]]]],\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - " [[[[[[[[[ True]]]]]]]]],\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - " [[[[[[[[[False]]]]]]]]],\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - " [[[[[[[[[ True]]]]]]]]],\n", - 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" 9.9998e-01, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 1.0014e-10,\n", - " 0.0000e+00, 9.9998e-01, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 1.0014e-10, 0.0000e+00, 1.0014e-10, 0.0000e+00, 1.0014e-10, 1.0014e-10,\n", - " 0.0000e+00, 1.0014e-10, 0.0000e+00, 0.0000e+00, 1.0014e-10, 1.0014e-10,\n", - " 9.9998e-01, 0.0000e+00, 9.9998e-01, 1.0014e-10, 0.0000e+00, 1.0014e-10,\n", - " 1.0014e-10, 1.0014e-10, 0.0000e+00, 0.0000e+00, 9.9998e-01, 1.0014e-10,\n", - " 1.0014e-10, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 1.0014e-10,\n", - " 0.0000e+00, 1.0014e-10, 1.0014e-10, 0.0000e+00, 1.0014e-10, 0.0000e+00,\n", - " 1.0014e-10, 0.0000e+00, 0.0000e+00, 1.0014e-10, 0.0000e+00, 1.0014e-10,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 1.0014e-10, 0.0000e+00,\n", - " 9.9998e-01, 1.0014e-10, 1.0014e-10, 1.0014e-10, 1.0014e-10, 0.0000e+00,\n", - " 9.9998e-01, 0.0000e+00, 1.0014e-10, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 1.0014e-10, 0.0000e+00, 1.0014e-10, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 1.0014e-10, 9.9998e-01, 9.9998e-01, 0.0000e+00, 1.0014e-10,\n", - " 0.0000e+00, 0.0000e+00, 1.0014e-10, 0.0000e+00, 1.0014e-10, 1.0014e-10,\n", - " 9.9998e-01, 1.0014e-10, 0.0000e+00, 0.0000e+00, 1.0014e-10, 0.0000e+00,\n", - " 1.0014e-10, 1.0014e-10, 1.0014e-10, 0.0000e+00, 9.9998e-01, 1.0014e-10,\n", - " 1.0014e-10, 0.0000e+00, 0.0000e+00, 0.0000e+00, 9.9998e-01, 0.0000e+00,\n", - " 0.0000e+00, 9.9998e-01, 0.0000e+00, 1.0014e-10, 1.0014e-10, 1.0014e-10,\n", - " 9.9998e-01, 1.0014e-10, 1.0014e-10, 1.0014e-10, 1.0014e-10, 1.0014e-10,\n", - " 1.0014e-10, 9.9998e-01, 0.0000e+00, 1.0014e-10, 0.0000e+00, 9.9998e-01,\n", - " 1.0014e-10, 0.0000e+00, 1.0014e-10, 1.0014e-10, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 1.0014e-10, 1.0014e-10, 9.9998e-01, 1.0014e-10, 0.0000e+00,\n", - " 1.0014e-10, 1.0014e-10, 0.0000e+00, 9.9998e-01, 0.0000e+00, 0.0000e+00,\n", - " 1.0014e-10, 0.0000e+00, 9.9998e-01, 0.0000e+00, 1.0014e-10, 1.0014e-10,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 9.9998e-01,\n", - " 0.0000e+00, 1.0014e-10, 1.0014e-10, 0.0000e+00, 0.0000e+00, 1.0014e-10,\n", - " 1.0014e-10, 9.9998e-01, 0.0000e+00, 1.0014e-10, 1.0014e-10, 1.0014e-10,\n", - " 1.0014e-10, 0.0000e+00, 1.0014e-10, 0.0000e+00, 1.0014e-10, 0.0000e+00,\n", - " 9.9998e-01, 1.0014e-10, 0.0000e+00, 0.0000e+00, 1.0014e-10, 0.0000e+00,\n", - " 1.0014e-10, 0.0000e+00, 1.0014e-10, 1.0014e-10, 1.0014e-10, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 1.0014e-10, 1.0014e-10,\n", - " 1.0014e-10, 0.0000e+00, 0.0000e+00, 9.9998e-01, 9.9998e-01, 1.0014e-10,\n", - " 0.0000e+00, 1.0014e-10, 9.9998e-01, 9.9998e-01, 1.0014e-10, 1.0014e-10,\n", - " 1.0014e-10, 1.0014e-10, 1.0014e-10, 9.9998e-01, 1.0014e-10, 0.0000e+00,\n", - " 9.9998e-01, 0.0000e+00, 0.0000e+00, 1.0014e-10, 1.0014e-10, 1.0014e-10,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 1.0014e-10, 1.0014e-10,\n", - " 1.0014e-10, 1.0014e-10, 0.0000e+00, 1.0014e-10, 1.0014e-10, 1.0014e-10,\n", - " 1.0014e-10, 9.9998e-01, 0.0000e+00, 1.0014e-10, 9.9998e-01, 1.0014e-10,\n", - " 0.0000e+00, 0.0000e+00, 1.0014e-10, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 1.0014e-10, 9.9998e-01, 0.0000e+00, 0.0000e+00, 1.0014e-10, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 9.9998e-01, 0.0000e+00, 9.9998e-01, 0.0000e+00,\n", - " 1.0014e-10, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 1.0014e-10, 1.0014e-10, 1.0014e-10, 0.0000e+00, 9.9998e-01, 9.9998e-01,\n", - " 0.0000e+00, 0.0000e+00, 1.0014e-10, 0.0000e+00, 0.0000e+00, 1.0014e-10,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 9.9998e-01, 9.9998e-01,\n", - " 1.0014e-10, 0.0000e+00, 0.0000e+00, 1.0014e-10, 1.0014e-10, 1.0014e-10,\n", - " 0.0000e+00, 0.0000e+00, 1.0014e-10, 0.0000e+00, 1.0014e-10, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 9.9998e-01, 1.0014e-10, 0.0000e+00, 1.0014e-10,\n", - " 9.9998e-01, 1.0014e-10, 0.0000e+00, 1.0014e-10, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 9.9998e-01, 0.0000e+00, 1.0014e-10, 0.0000e+00,\n", - " 1.0014e-10, 1.0014e-10, 1.0014e-10, 0.0000e+00, 1.0014e-10, 1.0014e-10,\n", - " 0.0000e+00, 1.0014e-10, 0.0000e+00, 9.9998e-01, 0.0000e+00, 1.0014e-10,\n", - " 0.0000e+00, 1.0014e-10, 1.0014e-10, 0.0000e+00, 0.0000e+00, 1.0014e-10,\n", - " 0.0000e+00, 1.0014e-10, 1.0014e-10, 1.0014e-10, 1.0014e-10, 0.0000e+00,\n", - " 0.0000e+00, 1.0014e-10, 0.0000e+00, 1.0014e-10, 1.0014e-10, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 1.0014e-10, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 1.0014e-10, 1.0014e-10, 9.9998e-01, 9.9998e-01,\n", - " 1.0014e-10, 0.0000e+00, 0.0000e+00, 1.0014e-10, 0.0000e+00, 1.0014e-10,\n", - " 0.0000e+00, 1.0014e-10, 1.0014e-10, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 1.0014e-10, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 1.0014e-10, 1.0014e-10, 1.0014e-10, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 1.0014e-10, 0.0000e+00, 0.0000e+00, 0.0000e+00, 1.0014e-10, 1.0014e-10,\n", - " 0.0000e+00, 9.9998e-01, 0.0000e+00, 9.9998e-01, 0.0000e+00, 1.0014e-10,\n", - " 1.0014e-10, 0.0000e+00, 9.9998e-01, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 9.9998e-01, 1.0014e-10, 0.0000e+00, 1.0014e-10, 9.9998e-01, 9.9998e-01,\n", - " 1.0014e-10, 0.0000e+00, 0.0000e+00, 1.0014e-10, 0.0000e+00, 0.0000e+00,\n", - " 1.0014e-10, 0.0000e+00, 9.9998e-01, 9.9998e-01, 1.0014e-10, 0.0000e+00,\n", - " 0.0000e+00, 9.9998e-01, 1.0014e-10, 1.0014e-10, 0.0000e+00, 0.0000e+00,\n", - " 1.0014e-10, 1.0014e-10, 0.0000e+00, 1.0014e-10, 0.0000e+00, 1.0014e-10,\n", - " 0.0000e+00, 0.0000e+00, 9.9998e-01, 9.9998e-01, 1.0014e-10, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 9.9998e-01, 0.0000e+00, 1.0014e-10, 1.0014e-10,\n", - " 0.0000e+00, 1.0014e-10, 1.0014e-10, 1.0014e-10, 1.0014e-10, 0.0000e+00,\n", - " 1.0014e-10, 0.0000e+00, 1.0014e-10, 1.0014e-10, 9.9998e-01, 1.0014e-10,\n", - " 0.0000e+00, 0.0000e+00, 1.0014e-10, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 9.9998e-01, 0.0000e+00,\n", - " 0.0000e+00, 1.0014e-10, 0.0000e+00, 1.0014e-10, 0.0000e+00, 1.0014e-10,\n", - " 1.0014e-10, 0.0000e+00, 0.0000e+00, 0.0000e+00, 9.9998e-01, 1.0014e-10,\n", - " 0.0000e+00, 1.0014e-10, 0.0000e+00, 0.0000e+00, 1.0014e-10, 9.9998e-01,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 9.9998e-01,\n", - " 1.0014e-10, 0.0000e+00, 0.0000e+00, 1.0014e-10, 1.0014e-10, 1.0014e-10,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 1.0014e-10, 0.0000e+00,\n", - " 0.0000e+00, 1.0014e-10, 0.0000e+00, 0.0000e+00, 1.0014e-10, 1.0014e-10,\n", - " 9.9998e-01, 1.0014e-10, 1.0014e-10, 9.9998e-01, 0.0000e+00, 0.0000e+00,\n", - " 9.9998e-01, 1.0014e-10, 9.9998e-01, 0.0000e+00, 1.0014e-10, 0.0000e+00,\n", - " 9.9998e-01, 0.0000e+00, 9.9998e-01, 1.0014e-10, 1.0014e-10, 1.0014e-10,\n", - " 1.0014e-10, 0.0000e+00, 0.0000e+00, 1.0014e-10, 0.0000e+00, 1.0014e-10,\n", - " 0.0000e+00, 1.0014e-10, 9.9998e-01, 1.0014e-10, 0.0000e+00, 0.0000e+00,\n", - " 1.0014e-10, 0.0000e+00, 1.0014e-10, 0.0000e+00, 1.0014e-10, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 1.0014e-10, 0.0000e+00, 1.0014e-10, 9.9998e-01,\n", - " 0.0000e+00, 0.0000e+00, 1.0014e-10, 0.0000e+00, 9.9998e-01, 0.0000e+00,\n", - " 1.0014e-10, 9.9998e-01, 9.9998e-01, 1.0014e-10, 1.0014e-10, 9.9998e-01,\n", - " 9.9998e-01, 0.0000e+00, 1.0014e-10, 0.0000e+00, 1.0014e-10, 1.0014e-10,\n", - " 0.0000e+00, 0.0000e+00, 9.9998e-01, 0.0000e+00, 1.0014e-10, 1.0014e-10,\n", - " 0.0000e+00, 1.0014e-10, 0.0000e+00, 1.0014e-10, 9.9998e-01, 0.0000e+00,\n", - " 1.0014e-10, 1.0014e-10, 1.0014e-10, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 1.0014e-10, 0.0000e+00, 0.0000e+00, 0.0000e+00, 1.0014e-10, 0.0000e+00,\n", - " 1.0014e-10, 0.0000e+00, 0.0000e+00, 0.0000e+00, 1.0014e-10, 0.0000e+00,\n", - " 1.0014e-10, 1.0014e-10, 1.0014e-10, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 9.9998e-01, 1.0014e-10, 1.0014e-10, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 1.0014e-10, 0.0000e+00, 1.0014e-10, 1.0014e-10,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 1.0014e-10, 9.9998e-01, 9.9998e-01,\n", - " 0.0000e+00, 0.0000e+00, 1.0014e-10, 1.0014e-10, 1.0014e-10, 0.0000e+00,\n", - " 1.0014e-10, 0.0000e+00, 1.0014e-10, 0.0000e+00, 9.9998e-01, 1.0014e-10,\n", - " 1.0014e-10, 9.9998e-01, 1.0014e-10, 1.0014e-10, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 1.0014e-10, 9.9998e-01, 0.0000e+00, 0.0000e+00,\n", - " 1.0014e-10, 0.0000e+00, 1.0014e-10, 1.0014e-10, 1.0014e-10, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 1.0014e-10, 9.9998e-01, 1.0014e-10, 0.0000e+00,\n", - " 1.0014e-10, 1.0014e-10, 1.0014e-10, 0.0000e+00, 0.0000e+00, 1.0014e-10,\n", - " 0.0000e+00, 1.0014e-10, 1.0014e-10, 1.0014e-10, 0.0000e+00, 1.0014e-10,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 1.0014e-10, 0.0000e+00, 0.0000e+00,\n", - " 1.0014e-10, 0.0000e+00, 0.0000e+00, 1.0014e-10, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 1.0014e-10, 1.0014e-10, 1.0014e-10, 1.0014e-10, 1.0014e-10,\n", - " 1.0014e-10, 0.0000e+00, 0.0000e+00, 0.0000e+00, 1.0014e-10, 9.9998e-01,\n", - " 1.0014e-10, 9.9998e-01, 0.0000e+00, 0.0000e+00, 0.0000e+00, 1.0014e-10,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 1.0014e-10, 1.0014e-10,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 1.0014e-10, 0.0000e+00,\n", - " 0.0000e+00, 1.0014e-10, 1.0014e-10, 1.0014e-10, 0.0000e+00, 1.0014e-10,\n", - " 0.0000e+00, 1.0014e-10, 0.0000e+00, 0.0000e+00, 9.9998e-01, 1.0014e-10,\n", - " 1.0014e-10, 0.0000e+00, 1.0014e-10, 1.0014e-10, 9.9998e-01, 0.0000e+00,\n", - " 0.0000e+00, 1.0014e-10, 0.0000e+00, 0.0000e+00, 9.9998e-01, 0.0000e+00,\n", - " 0.0000e+00, 1.0014e-10, 0.0000e+00, 0.0000e+00, 9.9998e-01, 1.0014e-10,\n", - " 1.0014e-10, 1.0014e-10, 9.9998e-01, 1.0014e-10, 1.0014e-10, 0.0000e+00,\n", - " 1.0014e-10, 0.0000e+00, 1.0014e-10, 9.9998e-01, 1.0014e-10, 0.0000e+00,\n", - " 9.9998e-01, 1.0014e-10, 0.0000e+00, 1.0014e-10, 0.0000e+00, 1.0014e-10,\n", - " 9.9998e-01, 1.0014e-10, 0.0000e+00, 9.9998e-01, 9.9998e-01, 1.0014e-10,\n", - " 1.0014e-10, 0.0000e+00, 1.0014e-10, 9.9998e-01, 1.0014e-10, 0.0000e+00,\n", - " 0.0000e+00, 9.9998e-01, 0.0000e+00, 1.0014e-10, 9.9998e-01, 1.0014e-10,\n", - " 0.0000e+00, 0.0000e+00, 1.0014e-10, 0.0000e+00, 0.0000e+00, 1.0014e-10,\n", - " 1.0014e-10, 0.0000e+00, 9.9998e-01, 0.0000e+00, 0.0000e+00, 1.0014e-10,\n", - " 1.0014e-10, 1.0014e-10, 1.0014e-10, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 9.9998e-01, 1.0014e-10, 0.0000e+00,\n", - " 1.0014e-10, 0.0000e+00, 0.0000e+00, 1.0014e-10, 0.0000e+00, 1.0014e-10,\n", - " 1.0014e-10, 0.0000e+00, 0.0000e+00, 1.0014e-10])\n", - "tensor(117.9976)\n" + "tensor(0.2683)\n" ] } ], "source": [ - "mask_intervened = (trace.nodes[\"__cause____antecedent_bill_throws\"][\"value\"] == 0)\n", - "log_weight_vector=logw\n", - "a = mask_intervened.float().sum()\n", - "print(mask_intervened)\n", - "# mask_intervened = mask_intervened.float()\n", - "# mask_intervened[mask_intervened == 0.0] = 9.2\n", - "# mask_intervened[mask_intervened == 1.0] = 1.0\n", - "print(log_weight_vector)\n", - "print(mask_intervened.squeeze())\n", - "print(torch.exp(log_weight_vector) * mask_intervened.squeeze())\n", - "\n", - "print(torch.sum(torch.exp(log_weight_vector) * mask_intervened.squeeze()))" + "mask_intervened = (trace.nodes[\"__cause____antecedent_sally_throws\"][\"value\"] == 0)\n", + "print(torch.sum(torch.exp(log_weights) * mask_intervened.float().squeeze())/mask_intervened.float().sum())" ] }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 53, "metadata": {}, "outputs": [ { - "ename": "AttributeError", - "evalue": "'Trace' object has no attribute 'n'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[62], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mtrace\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mn\u001b[49m)\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(trace\u001b[38;5;241m.\u001b[39mnodes[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msally_throws\u001b[39m\u001b[38;5;124m\"\u001b[39m][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalue\u001b[39m\u001b[38;5;124m\"\u001b[39m][\u001b[38;5;241m2\u001b[39m]\u001b[38;5;241m.\u001b[39msqueeze())\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28mprint\u001b[39m(trace\u001b[38;5;241m.\u001b[39mnodes[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbottle_shatters\u001b[39m\u001b[38;5;124m\"\u001b[39m][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalue\u001b[39m\u001b[38;5;124m\"\u001b[39m][\u001b[38;5;241m2\u001b[39m]\u001b[38;5;241m.\u001b[39msqueeze())\n", - "\u001b[0;31mAttributeError\u001b[0m: 'Trace' object has no attribute 'n'" + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(0.2004)\n" ] } ], "source": [ - "print(trace.n)\n", - "print(trace.nodes[\"sally_throws\"][\"value\"][2].squeeze())\n", - "print(trace.nodes[\"bottle_shatters\"][\"value\"][2].squeeze())\n", - "trace.nodes[\"__cause____consequent_bottle_shatters\"][\"log_prob\"][2]" + "mask_intervened = (trace.nodes[\"__cause____antecedent_bill_throws\"][\"value\"] == 0)\n", + "print(torch.sum(torch.exp(log_weights) * mask_intervened.float().squeeze())/mask_intervened.float().sum())" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# References\n", - "\n", - "1. " + "Note that we assumed Sally to be more likely to throw, more likely to hit, and more likely to shatter the bottle if she hits. For this reason, we expect her to be more likely to be causally responsible for the outcome and that is the result we got. Conceptually, these estimates are impacted by some hyperparameters, such as witness preemption probabilities, so perhaps a bit more clarity on can be gained if we think we have a complete list of potential causes and normalize. " ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] } ], "metadata": { diff --git a/docs/source/responsibility.ipynb b/docs/source/responsibility.ipynb index a203dbc4..5607419f 100644 --- a/docs/source/responsibility.ipynb +++ b/docs/source/responsibility.ipynb @@ -88,14 +88,14 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.4971)\n" + "tensor(0.1057)\n" ] } ], @@ -106,6 +106,7 @@ " consequents={\"C\": torch.tensor(1.0)},\n", " witnesses={},\n", " alternatives={\"A\": 0.0, \"B\": 0.0},\n", + " antecedent_bias=0.4,\n", " consequent_scale=1e-5,\n", ")(example)\n", "\n", @@ -115,14 +116,14 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.5016)\n" + "tensor(0.1077)\n" ] } ],