Probabilities of Causation for Continuous and Vector Variables

Yuta Kawakami, manabu kuroki, Jin Tian

Keywords: Probabilities of causation, Continuous variables, Vector variables

TL;DR: In this paper, we extend the concept of the probabilities of causation to multiple continuous treatments and outcomes.

Abstract: *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 of PoC to continuous treatment and outcome variables, and further generalize PoC to capture causal effects between multiple treatments and multiple outcomes. In addition, we consider PoC for a sub-population and PoC with multi-hypothetical terms to capture more sophisticated counterfactual information useful for decision-making. We provide a nonparametric identification theorem for each type of PoC we introduce. Finally, we illustrate the application of our results on a real-world dataset about education.

List Of Authors: Kawakami, Yuta and Kuroki, Manabu and Tian, Jin

Submission Number: 638