Identification of Long-Term Treatment Effects via Temporal Links, Observational, and Experimental Data
Filip Obradovi\'c

TL;DR
This paper develops a new framework for identifying long-term treatment effects by combining experimental and observational data, emphasizing the importance of assumptions on temporal link functions and providing bounds on effects.
Contribution
It introduces a unifying identification framework with sharp bounds for long-term effects, incorporating plausible assumptions and handling imperfect compliance.
Findings
Long-term effects of Head Start are lasting but smaller than sibling comparison estimates.
The framework extends existing methods to accommodate imperfect experimental compliance.
Assumptions on temporal link functions are crucial for identification.
Abstract
Recent literature proposes combining short-term experimental and long-term observational data to provide alternatives to conventional observational studies for the identification of long-term average treatment effects (LTEs). This paper re-examines the identification problem and uncovers that assumptions restricting temporal link functions -- relationships between short-term and mean long-term potential outcomes -- are central in this context. The experimental data serve to amplify the identifying power of such assumptions; absent them, the combined data are no more informative than the observational data alone. Plausible inference thus hinges on justifiable restrictions in this class. Motivated by this, I introduce two treatment response assumptions that may be defensible based on economic theory or intuition. To utilize them and facilitate future developments, I develop a novel…
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Taxonomy
TopicsStatistical Methods and Inference · Advanced Causal Inference Techniques · Statistical Methods in Clinical Trials
