A nonparametric projection-based estimator for the probability of causation, with application to water sanitation in Kenya

TL;DR

This paper introduces a nonparametric influence-function-based estimator for the probability of causation, enabling more accurate and interpretable causal inference in water sanitation studies in Kenya.

Contribution

It develops a novel nonparametric estimator for probability of causation that requires weak assumptions and allows for valid inference, improving upon existing parametric methods.

Findings

Estimated probability of causation for high bacterial exposure is 0.12.

The estimator provides valid confidence intervals for causal attribution.

Application demonstrates the method's utility in real-world water sanitation data.

Abstract

Current estimation methods for the probability of causation (PC) make strong parametric assumptions or are inefficient. We derive a nonparametric influence-function-based estimator for a projection of PC, which allows for simple interpretation and valid inference by making weak structural assumptions. We apply our estimator to real data from an experiment in Kenya, which found, by estimating the average treatment effect, that protecting water springs reduces childhood disease. However, before scaling up this intervention, it is important to determine whether it was the exposure, and not something else, that caused the outcome. Indeed, we find that some children, who were exposed to a high concentration of bacteria in drinking water and had a diarrheal disease, would likely have contracted the disease absent the exposure since the estimated PC for an average child in this study is 0.12 with a 95% confidence interval of (0.11, 0.13). Our nonparametric method offers researchers a way to estimate PC, which is essential if one wishes to determine not only the average treatment effect, but also whether an exposure likely caused the observed outcome.