Continual Causal Effect Estimation: Challenges and Opportunities

TL;DR

This paper discusses the emerging challenges and opportunities in continual causal effect estimation, focusing on incremental data, domain adaptation, and data accessibility in observational studies.

Contribution

It formally defines the problem of continual treatment effect estimation and outlines potential solutions and future research directions.

Findings

Identifies key challenges in continual causal inference.

Proposes conceptual solutions for incremental data and domain adaptation.

Highlights future research avenues in the field.

Abstract

A further understanding of cause and effect within observational data is critical across many domains, such as economics, health care, public policy, web mining, online advertising, and marketing campaigns. Although significant advances have been made to overcome the challenges in causal effect estimation with observational data, such as missing counterfactual outcomes and selection bias between treatment and control groups, the existing methods mainly focus on source-specific and stationary observational data. Such learning strategies assume that all observational data are already available during the training phase and from only one source. This practical concern of accessibility is ubiquitous in various academic and industrial applications. That's what it boiled down to: in the era of big data, we face new challenges in causal inference with observational data, i.e., the extensibility for incrementally available observational data, the adaptability for extra domain adaptation problem except for the imbalance between treatment and control groups, and the accessibility for an enormous amount of data. In this position paper, we formally define the problem of continual treatment effect estimation, describe its research challenges, and then present possible solutions to this problem. Moreover, we will discuss future research directions on this topic.