- [[confounding variable vs risk factor]] # Idea Unmeasured attributes or variables that influence a relationship between two variables are known as **confounders**, **lurking variables**, or **unobserved heterogeneity**. They are often variables that undermine [[causal inference]]. In general, it's extremely difficult or even impossible (in the social sciences) to know what all the confounders are. ![[20250216180243.png]] Confounders cause/affect both the treatment and outcome. $X$ is a confounder: ```mermaid graph LR X --> T X --> Y T --> Y ``` If there are unmeasured/unobserved confounders that affect both the treatment assignment and outcome, we can get biased estimates of causal effects. The [[ignorability assumption]] is violated. But we can use methods like [[instrumental variables|instrumental variable analysis]] that do not rely on the [[ignorability assumption]]. To "address" the problem of unobserved confounders, researchers try to use methods that don't require them to identify or measure all potential confounders, and [[experiment|experimentation]] is a solution. # References - [[Gerber 2012 field experiments]] - [\| Codecademy](https://www.codecademy.com/courses/learn-the-basics-of-causal-inference-with-r/lessons/potential-outcomes-framework/exercises/confounders)