# A Survey of Causal Inference Frameworks

arXiv: https://arxiv.org/abs/2209.00869 · HTML: https://mlsanity.com/paper/2209.00869/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Sep 2022.

## Built on by (most cited first, 5 of 5)

- [Large Language Models and Causal Inference in Collaboration: A Survey](https://mlsanity.com/paper/2403.09606/) (2024)
- [Causal Effect Estimation: Recent Advances, Challenges, and Opportunities](https://mlsanity.com/paper/2302.00848/) (2023)
- [The Odyssey of Commonsense Causality: From Foundational Benchmarks to Cutting-Edge Reasoning](https://mlsanity.com/paper/2406.19307/) (2024)
- [Towards Fair and Explainable AI using a Human-Centered AI Approach](https://mlsanity.com/paper/2306.07427/) (2023)
- [Evaluating Large Language Models in Code Generation: INFINITE Methodology for Defining the Inference Index](https://mlsanity.com/paper/2503.05852/) (2025)

## Abstract (arXiv)

Causal inference is a science with multi-disciplinary evolution and applications. On the one hand, it measures effects of treatments in observational data based on experimental designs and rigorous statistical inference to draw causal statements. One of the most influential framework in quantifying causal effects is the potential outcomes framework. On the other hand, causal graphical models utilizes directed edges to represent causalities and encodes conditional independence relationships among variables in the graphs. A series of research has been done both in reading-off conditional independencies from graphs and in re-constructing causal structures. In recent years, the most state-of-art research in causal inference starts unifying the different causal inference frameworks together. This survey aims to provide a review of the past work on causal inference, focusing mainly on potential outcomes framework and causal graphical models. We hope that this survey will help accelerate the understanding of causal inference in different domains.