# Randomization does not imply unconfoundedness

arXiv: https://arxiv.org/abs/2107.14197 · HTML: https://mlsanity.com/paper/2107.14197/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jul 2021.

## Built on by (most cited first, 1 of 1)

- [Bayesian Supervised Causal Clustering](https://mlsanity.com/paper/2603.05288/) (2026)

## Abstract (arXiv)

A common assumption in causal inference is that random treatment assignment ensures that potential outcomes are independent of treatment, or in one word, unconfoundedness. This paper highlights that randomization and unconfoundedness are separate properties, and neither implies the other. A study with random treatment assignment does not have to be unconfounded, and a study with deterministic assignment can still be unconfounded. A corollary is that a propensity score is not the same thing as a treatment assignment probability. These facts should not be taken as arguments against randomization. The moral of this paper is that randomization is useful only when investigators know or can reconstruct the assignment process.