Regression modelQuasi-experimental / causal inference
Sensitivity Analysis for Causality
Sensitivity analysis for causality assesses how robust a causal conclusion is to unobserved confounding. Rather than assuming all confounders are controlled, it asks: how strong would an unmeasured variable need to be to overturn the estimated effect? It is an indispensable robustness check after any quasi-experimental or observational causal analysis.
Open in MethodMindSoonVideoSoon
Read the full method
Members only
Sign inSign in with a free account to read this section.
Sources
- Rosenbaum, P. R. (2002). Observational Studies (2nd ed.). Springer. ISBN: 978-0387989679
- Cinelli, C., & Hazlett, C. (2020). Making sense of sensitivity: Extending omitted variable bias. Journal of the Royal Statistical Society: Series B, 82(1), 39-67. DOI: 10.1111/rssb.12348 ↗
Related methods
Referenced by
Bayesian Placebo TestBayesian Sensitivity Analysis for CausalityHeterogeneous treatment effect Placebo testHeterogeneous Treatment Effect Sensitivity Analysis for CausalityPanel Data Placebo TestRobust Causal Impact AnalysisRobust Counterfactual Impact EvaluationRobust Propensity Score WeightingRobust Synthetic Control MethodSpatial Placebo Test