Causal Inference with Latent Variables: Recent Advances and Future Prospectives

TL;DR

This paper reviews recent advances in causal inference methods that address the challenge of unobserved latent variables, highlighting new strategies, applications, and future directions, especially in the context of large language models.

Contribution

It provides a comprehensive survey of techniques for causal inference with latent variables, including traditional, circumvention, and inference-based methods, and discusses future research opportunities.

Findings

Summarizes traditional CI techniques assuming fully observed variables.

Details methods for handling latent variables in various CI tasks.

Identifies new opportunities in large language models for causal inference.

Abstract

Causality lays the foundation for the trajectory of our world. Causal inference (CI), which aims to infer intrinsic causal relations among variables of interest, has emerged as a crucial research topic. Nevertheless, the lack of observation of important variables (e.g., confounders, mediators, exogenous variables, etc.) severely compromises the reliability of CI methods. The issue may arise from the inherent difficulty in measuring the variables. Additionally, in observational studies where variables are passively recorded, certain covariates might be inadvertently omitted by the experimenter. Depending on the type of unobserved variables and the specific CI task, various consequences can be incurred if these latent variables are carelessly handled, such as biased estimation of causal effects, incomplete understanding of causal mechanisms, lack of individual-level causal consideration, etc. In this survey, we provide a comprehensive review of recent developments in CI with latent variables. We start by discussing traditional CI techniques when variables of interest are assumed to be fully observed. Afterward, under the taxonomy of circumvention and inference-based methods, we provide an in-depth discussion of various CI strategies to handle latent variables, covering the tasks of causal effect estimation, mediation analysis, counterfactual reasoning, and causal discovery. Furthermore, we generalize the discussion to graph data where interference among units may exist. Finally, we offer fresh aspects for further advancement of CI with latent variables, especially new opportunities in the era of large language models (LLMs).