Generalized Encouragement-Based Instrumental Variables for Counterfactual Regression

TL;DR

This paper introduces a new method called EnCounteR for estimating causal effects in encouragement designs, effectively addressing challenges like incomplete randomization and limited data, and demonstrating superior performance on synthetic and real datasets.

Contribution

It develops novel theories and algorithms for identifying Conditional Average Treatment Effects using encouragement variations and proposes a generalized IV estimator called EnCounteR.

Findings

EnCounteR outperforms existing methods in experiments.

The approach effectively leverages observational and encouragement data.

Theoretical guarantees for causal effect identification are provided.

Abstract

In causal inference, encouragement designs (EDs) are widely used to analyze causal effects, when randomized controlled trials (RCTs) are impractical or compliance to treatment cannot be perfectly enforced. Unlike RCTs, which directly allocate treatments, EDs randomly assign encouragement policies that positively motivate individuals to engage in a specific treatment. These random encouragements act as instrumental variables (IVs), facilitating the identification of causal effects through leveraging exogenous perturbations in discrete treatment scenarios. However, real-world applications of encouragement designs often face challenges such as incomplete randomization, limited experimental data, and significantly fewer encouragements compared to treatments, hindering precise causal effect estimation. To address this, this paper introduces novel theories and algorithms for identifying the Conditional Average Treatment Effect (CATE) using variations in encouragement. Further, by leveraging both observational and encouragement data, we propose a generalized IV estimator, named Encouragement-based Counterfactual Regression (EnCounteR), to effectively estimate the causal effects. Extensive experiments on both synthetic and real-world datasets demonstrate the superiority of EnCounteR over existing methods.