Probabilities of causation and post-infection outcomes
Probabilities of causation provide explanatory information on the observed occurrence (causal necessity) and non-occurrence (causal sufficiency) of events. Here, we adapt these probabilities (probability of necessity, probability of sufficiency, and probability of necessity and sufficiency) to an important class of epidemiologic outcomes, post-infection outcomes. A defining feature of studies on these outcomes is that they account for the post-treatment variable, infection acquisition, which means that, for individuals who remain uninfected, the outcome is not defined. Following previous work by Hudgens and Halloran, we describe analyses of post-infection outcomes using the principal stratification framework, and then derive expressions for the probabilities of causation in terms of principal strata-related parameters. Finally, we show that these expressions provide insights into the contributions of different processes (absence or occurrence of infection, and disease severity), implicitly encoded in the definition of the outcome, to causation.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Identification of Probabilities of Causation: A Complete Characterization
Probabilities of causation are fundamental to modern decision-making. Pearl first introduced three binary probabilities of causation, and Tian and Pearl later derived tight bounds for them using Balke's linear programmin…
Decision MakingMathematical ProofsProbabilities of Causation for Continuous and Vector Variables
Probabilities of causation (PoC) are valuable concepts for explainable artificial intelligence and practical decision-making. PoC are originally defined for scalar binary variables. In this paper, we extend the concept o…
counterfactualDecision MakingExplainable artificial intelligenceEstimating Probabilities of Causation with Machine Learning Models
Probabilities of causation play a crucial role in modern decision-making. This paper addresses the challenge of predicting probabilities of causation for subpopulations with insufficient data using machine learning model…
Learning Probabilities of Causation from Finite Population Data
This paper deals with the problem of learning the probabilities of causation of subpopulations given finite population data. The tight bounds of three basic probabilities of causation, the probability of necessity and su…
Bounding Probabilities of Causation with Partial Causal Diagrams
Probabilities of causation are fundamental to individual-level explanation and decision making, yet they are inherently counterfactual and not point-identifiable from data in general. Existing bounds either disregard ava…
Decision Making