Optional dynamic treatment regimes and partial welfare ordering

Arbeitspapier

Dynamic treatment regimes are treatment allocations tailored to heterogeneous individuals. The optimal dynamic treatment regime is a regime that maximizes counterfactual welfare. We introduce a framework in which we can partially learn the optimal dynamic regime from observational data, relaxing the sequential randomization assumption commonly employed in the literature but instead using (binary) instrumental variables. We propose the notion of sharp partial ordering of counterfactual welfares with respect to dynamic regimes and establish mapping from data to partial ordering via a set of linear programs. We then characterize the identified set of the optimal regime as the set of maximal elements associated with the partial ordering. One main contribution of this paper is that we develop simple analytical conditions to establish the ordering, which bypass solving a large number of large-scale linear programs, and thus facilitate estimation and inference. This paper's analytical framework has broader applicability beyond the current context, e.g., in establishing signs of various treatment effects and rankings of policies across different counterfactual scenarios.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: cemmap working paper ; No. CWP50/20

Classification and Topics

• Classification: Wirtschaft ; Semiparametric and Nonparametric Methods: General ; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models ; Multiple or Simultaneous Equation Models: Panel Data Models; Spatio-temporal Models ; Multiple or Simultaneous Equation Models: Instrumental Variables (IV) Estimation
• Subject: Optimal dynamic treatment regimes ; endogenous treatments ; dynamic treatment effect ; partial identification ; instrumental variable ; linear programming

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Han, Sukjin
• Event: Veröffentlichung
• (who): Centre for Microdata Methods and Practice (cemmap)
• (where): London
• (when): 2020

Further information

• DOI: doi:10.47004/wp.cem.2020.5020
• Handle: http://hdl.handle.net/10419/241925
• Last update: 10.03.2025, 11:43 AM CET