Stochastic Intervention for Causal Effect Estimation

TL;DR

This paper introduces a novel approach for estimating stochastic intervention effects in causal inference, using a new propensity score, a specialized estimator, and a genetic algorithm to improve decision-making insights.

Contribution

It develops a stochastic intervention effect estimator and a genetic algorithm tailored for stochastic policies, addressing limitations of existing deterministic methods.

Findings

Proposed measures outperform state-of-the-art baselines.

Theoretical analysis supports the effectiveness of the new methods.

Empirical results show significant performance improvements.

Abstract

Causal inference methods are widely applied in various decision-making domains such as precision medicine, optimal policy and economics. Central to these applications is the treatment effect estimation of intervention strategies. Current estimation methods are mostly restricted to the deterministic treatment, which however, is unable to address the stochastic space treatment policies. Moreover, previous methods can only make binary yes-or-no decisions based on the treatment effect, lacking the capability of providing fine-grained effect estimation degree to explain the process of decision making. In our study, we therefore advance the causal inference research to estimate stochastic intervention effect by devising a new stochastic propensity score and stochastic intervention effect estimator (SIE). Meanwhile, we design a customized genetic algorithm specific to stochastic intervention effect (Ge-SIO) with the aim of providing causal evidence for decision making. We provide the theoretical analysis and conduct an empirical study to justify that our proposed measures and algorithms can achieve a significant performance lift in comparison with state-of-the-art baselines.