Causes of Effects: Learning individual responses from population data

• URL: http://arxiv.org/abs/2104.13730v1
• Date: Wed, 28 Apr 2021 12:38:11 GMT
• Title: Causes of Effects: Learning individual responses from population data
• Authors: Scott Mueller, Ang Li, Judea Pearl
• Abstract summary: We study the problem of individualization and its applications in medicine. For example, the probability of benefiting from a treatment concerns an individual having a favorable outcome if treated and an unfavorable outcome if untreated. We analyze and expand on existing research by applying bounds to the probability of necessity and sufficiency (PNS) along with graphical criteria and practical applications.
• Score: 23.593582720307207
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The problem of individualization is recognized as crucial in almost every field. Identifying causes of effects in specific events is likewise essential for accurate decision making. However, such estimates invoke counterfactual relationships, and are therefore indeterminable from population data. For example, the probability of benefiting from a treatment concerns an individual having a favorable outcome if treated and an unfavorable outcome if untreated. Experiments conditioning on fine-grained features are fundamentally inadequate because we can't test both possibilities for an individual. Tian and Pearl provided bounds on this and other probabilities of causation using a combination of experimental and observational data. Even though those bounds were proven tight, narrower bounds, sometimes significantly so, can be achieved when structural information is available in the form of a causal model. This has the power to solve central problems, such as explainable AI, legal responsibility, and personalized medicine, all of which demand counterfactual logic. We analyze and expand on existing research by applying bounds to the probability of necessity and sufficiency (PNS) along with graphical criteria and practical applications.

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