Bayesian causal inference for count potential outcomes

TL;DR

This paper integrates Bayesian causal inference methods with count data models to improve estimation of treatment effects for outcomes that are non-negative integers, offering new insights and generalizations.

Contribution

It introduces a framework for Bayesian causal inference tailored to count data, extending existing methods and discussing architectural considerations for predictive modeling.

Findings

Provides a Bayesian approach for count potential outcomes

Generalizes relationships for estimating average treatment effects

Discusses architectural considerations for predictive posterior

Abstract

The literature for count modeling provides useful tools to conduct causal inference when outcomes take non-negative integer values. Applied to the potential outcomes framework, we link the Bayesian causal inference literature to statistical models for count data. We discuss the general architectural considerations for constructing the predictive posterior of the missing potential outcomes. Special considerations for estimating average treatment effects are discussed, some generalizing certain relationships and some not yet encountered in the causal inference literature.