Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/260379 
Year of Publication: 
2021
Series/Report no.: 
cemmap working paper No. CWP43/21
Publisher: 
Centre for Microdata Methods and Practice (cemmap), London
Abstract: 
We provide novel bounds on average treatment effects (on the treated) that are valid under an unconfoundedness assumption. Our bounds are designed to be robust in challenging situations, for example, when the conditioning variables take on a large number of different values in the observed sample, or when the overlap condition is violated. This robustness is achieved by only using limited "pooling" of information across observations. Namely, the bounds are constructed as sample averages over functions of the observed outcomes such that the contribution of each outcome only depends on the treatment status of a limited number of observations. No information pooling across observations leads to so-called "Manski bounds", while unlimited information pooling leads to standard inverse propensity score weighting. We explore the intermediate range between these two extremes and provide corresponding inference methods. We show in Monte Carlo experiments and through an empirical application that our bounds are indeed robust and informative in practice.
Subjects: 
Causal Inference
Unconfoundedness
Limited Overlap
Partial Identification
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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