# When Is Causal Inference Possible? A Statistical Test for Unmeasured Confounding

## TL;DR

This paper introduces a statistical test to determine when causal effects can be identified from observational data by assessing unmeasured confounding, bridging potential outcomes and causal graph frameworks.

## Contribution

It proposes a novel test for unconfoundedness that compares randomized and observational datasets, enabling practical evaluation of causal inference validity.

## Key findings

- The test controls Type I error probability.
- Analysis of power under linear models.
- Provides a practical method for real-world data assessment.

## Abstract

This paper clarifies a fundamental difference between causal inference and traditional statistical inference by formalizing a mathematical distinction between their respective parameters. We connect two major approaches to causal inference, the potential outcomes framework and causal structure graphs, which are typically studied separately. While the unconfoundedness assumption in the potential outcomes framework cannot be assessed from an observational dataset alone, causal structure graphs help explain when causal effects are identifiable through graphical models. We propose a statistical test to assess the unconfoundedness assumption, equivalent to the absence of unmeasured confounding, by comparing two datasets: a randomized controlled trial and an observational study. The test controls the Type I error probability, and we analyze its power under linear models. Our approach provides a practical method to evaluate when real-world data are suitable for causal inference.