Causal inference for binary non-independent outcomes

TL;DR

This paper introduces a novel causal inference framework for binary outcomes that are non-independent, utilizing a log-mean linear regression approach to decompose effects into intrinsic and extrinsic components, demonstrated through two randomized experiments.

Contribution

It develops a new causal inference method for binary non-independent outcomes using product outcomes and a log-mean linear model, enabling easy derivation of causal effects.

Findings

Effective modeling of joint binary outcomes with dependence structure.

Clear decomposition of causal effects into intrinsic and extrinsic components.

Application to real randomized experiments demonstrating practical utility.

Abstract

Causal inference on multiple non-independent outcomes raises serious challenges, because multivariate techniques that properly account for the outcome's dependence structure need to be considered. We focus on the case of binary outcomes framing our discussion in the potential outcome approach to causal inference. We define causal effects of treatment on joint outcomes introducing the notion of product outcomes. We also discuss a decomposition of the causal effect on product outcomes into intrinsic and extrinsic causal effects, which respectively provide information on treatment effect on the intrinsic (product) structure of the product outcomes and on the outcomes' dependence structure. We propose a log-mean linear regression approach for modeling the distribution of the potential outcomes, which is particularly appealing because all the causal estimands of interest and the decomposition into intrinsic and extrinsic causal effects can be easily derived by model parameters. The method is illustrated in two randomized experiments concerning (i) the effect of the administration of oral pre-surgery morphine on pain intensity after surgery; and (ii) the effect of honey on nocturnal cough and sleep difficulty associated with childhood upper respiratory tract infections.