Active and Passive Causal Inference Learning

TL;DR

This paper introduces foundational concepts and categorizes key causal inference techniques into active and passive approaches, providing a comprehensive starting point for learners and researchers in causal inference.

Contribution

It offers a structured overview of causal inference assumptions and methods, including classical, active, and deep learning approaches, highlighting areas for further research.

Findings

Categorizes causal inference methods into active and passive approaches.

Discusses assumptions like exchangeability, positivity, and consistency.

Highlights recent deep learning algorithms and missing aspects like collider biases.

Abstract

This paper serves as a starting point for machine learning researchers, engineers and students who are interested in but not yet familiar with causal inference. We start by laying out an important set of assumptions that are collectively needed for causal identification, such as exchangeability, positivity, consistency and the absence of interference. From these assumptions, we build out a set of important causal inference techniques, which we do so by categorizing them into two buckets; active and passive approaches. We describe and discuss randomized controlled trials and bandit-based approaches from the active category. We then describe classical approaches, such as matching and inverse probability weighting, in the passive category, followed by more recent deep learning based algorithms. By finishing the paper with some of the missing aspects of causal inference from this paper, such as collider biases, we expect this paper to provide readers with a diverse set of starting points for further reading and research in causal inference and discovery.