# Accounting for hidden common causes when inferring cause and effect from observational data

arXiv: https://arxiv.org/abs/1801.00727 · HTML: https://mlsanity.com/paper/1801.00727/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jan 2018.

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

- [Quantum causal inference in the presence of hidden common causes: An entropic approach](https://mlsanity.com/paper/2104.13227/) (2021)

## Abstract (arXiv)

Identifying causal relationships from observation data is difficult, in large part, due to the presence of hidden common causes. In some cases, where just the right patterns of conditional independence and dependence lie in the data---for example, Y-structures---it is possible to identify cause and effect. In other cases, the analyst deliberately makes an uncertain assumption that hidden common causes are absent, and infers putative causal relationships to be tested in a randomized trial. Here, we consider a third approach, where there are sufficient clues in the data such that hidden common causes can be inferred.