Introducing the CP-plot for Causal Inference with Observational Studies

Abstract

Under the canonical setting of observational studies for causal inference, we derive a set of exact representations for pairwise differences among weighted average treatment effects as covariances between the conditional average treatment effect and the propensity score, up to positive scaling factors. These covariance representations bridge the two core concepts in causal inference with observational studies. They immediately imply that (i) the average treatment effect is bracketed by the average treatment effects on the treated and on the controls, with the direction determined by the sign of the covariance between the conditional average treatment effect and the propensity score, and (ii) the average treatment effect under the overlap weight, the weight that is proportional to the conditional variance of the treatment given the covariates, is bracketed by the average treatment effects on the treated and controls when the corresponding covariances have a common sign within both the treated and control groups. We further extend these results to weighted local average treatment effects in the instrumental variable framework. Building on this theory, we recommend the ``CP-plot'' of the estimated conditional average treatment effect against the estimated propensity score, and implement it in the R package \texttt{CPplot}.