#partial-identification

2 notes

• Causal Inference Under Partial Identification — Sensitivity and Evidence Hierarchies When real-world data fail strong ignorability, point identification gives way to bounds, proxies, and sensitivity analysis — an honest hierarchy of evidence that connects credible causal claims to semiparametric efficiency.
• Partial Identification When point identification is impossible due to a lack of assumptions, we only know that the parameter lies in the identified set $\ThetaI$ (often an interval $[\thetaL,\thetaU]$) compatible with the data plus assumptions. Manski's assumption-free / worst-case bounds are the starting point. sharp bounds =…