  This research addresses the challenge of conducting interpretable causal
inference between a binary treatment and its resulting outcome when not all
confounders are known. Confounders are factors that have an influence on both
the treatment and the outcome. We relax the requirement of knowing all
confounders under desired treatment, which we refer to as Selective
Confounding, to enable causal inference in diverse real-world scenarios. Our
proposed scheme is designed to work in situations where multiple
decision-makers with different policies are involved and where there is a
re-evaluation mechanism after the initial decision to ensure consistency. These
assumptions are more practical to fulfill compared to the availability of all
confounders under all treatments. To tackle the issue of Selective Confounding,
we propose the use of dual-treatment samples. These samples allow us to employ
two-step procedures, such as Regression Adjustment or Doubly-Robust, to learn
counterfactual predictors. We provide both theoretical error bounds and
empirical evidence of the effectiveness of our proposed scheme using synthetic
and real-world child placement data. Furthermore, we introduce three evaluation
methods specifically tailored to assess the performance in child placement
scenarios. By emphasizing transparency and interpretability, our approach aims
to provide decision-makers with a valuable tool. The source code repository of
this work is located at https://github.com/sohaib730/CausalML.
