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Robust Learning for Optimal Dynamic Treatment Regimes with Observational Data
Many public policies and medical interventions involve dynamics in their treatment assignments, where treatments are sequentially assigned to the same individuals across multiple stages, and the effect of treatment at each stage is usually heterogeneous with respect to the history of prior treatments and associated characteristics.
Collaborative knowledge exchange promotes innovation
Considering collaborative patent development, we provide micro-level evidence for innovation through exchanges of differentiated knowledge.
Paternalism, Autonomy, or Both? Experimental Evidence from Energy Saving Programs
Our estimates confirm that the estimated assignment policy optimally allocates individuals to be treated, untreated, or choose themselves based on the relative merits of paternalistic assignments and autonomous choice for individuals types.
Partial Identification and Inference in Duration Models with Endogenous Censoring
In this setting, we develop bounds on the regression parameters and the transformation function, which are characterized by conditional moment inequalities involving U-statistics.
Constrained Classification and Policy Learning
Consistency of the surrogate loss approaches studied in Zhang (2004) and Bartlett et al. (2006) crucially relies on the assumption of correct specification, meaning that the specified set of classifiers is rich enough to contain a first-best classifier.
Estimation of Optimal Dynamic Treatment Assignment Rules under Policy Constraints
This paper proposes an empirical welfare maximization approach in a dynamic framework.
