How Conditioning on Posttreatment Variables Can Ruin Your Experiment and What to Do about It

699 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

No Harm in Checking: Using Factual Manipulation Checks to Assess Attentiveness in Experiments (2018, background): “…use FMCs to conduct analyses on “passers” only, this practice raises concerns regarding posttreatment bias (e.g., Acharya, Blackwell, and Sen 2016; Montgomery, Nyhan, and Torres 2018) and/or unrepresentative samples (Berinsky, Margolis, and Sances 2014, 747–48). a treatment-irrelevant FMC…”
Deepfakes and Disinformation: Exploring the Impact of Synthetic Political Video on Deception, Uncertainty, and Trust in News (2020, background): “As possible solutions, they cite designs that include “a treatment that affects the mediator but not the outcome” (Montgomery et al., 2018, p. 772).”
Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates (2020, background): “By contrast, conditioning on a collider can create dependence between treatment-collider-outcome5 (Pearl, 2009a) potentially introducing more bias (Montgomery et al., 2018; Elwert and Winship, 2014).”