Generalized Encouragement-Based Instrumental Variables for Counterfactual Regression
This work addresses the problem of precise causal effect estimation for researchers and practitioners in causal inference, particularly in scenarios with incomplete randomization and limited data, representing an incremental advancement in methodology.
The paper tackles the challenge of estimating causal effects in encouragement designs, where randomized controlled trials are impractical, by introducing a generalized instrumental variable estimator called EnCounteR that leverages both observational and encouragement data, and demonstrates its superiority over existing methods in experiments on synthetic and real-world datasets.
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.