ﻻ يوجد ملخص باللغة العربية
Consider a planner who has to decide whether or not to introduce a new policy to a certain local population. The planner has only limited knowledge of the policys causal impact on this population due to a lack of data but does have access to the publicized results of intervention studies performed for similar policies on different populations. How should the planner make use of and aggregate this existing evidence to make her policy decision? Building upon the paradigm of `patient-centered meta-analysis proposed by Manski (2020; Towards Credible Patient-Centered Meta-Analysis, Epidemiology), we formulate the planners problem as a statistical decision problem with a social welfare objective pertaining to the local population, and solve for an optimal aggregation rule under the minimax-regret criterion. We investigate the analytical properties, computational feasibility, and welfare regret performance of this rule. We also compare the minimax regret decision rule with plug-in decision rules based upon a hierarchical Bayes meta-regression or stylized mean-squared-error optimal prediction. We apply the minimax regret decision rule to two settings: whether to enact an active labor market policy given evidence from 14 randomized control trial studies; and whether to approve a drug (Remdesivir) for COVID-19 treatment using a meta-database of clinical trials.
We consider the setting in which a strong binary instrument is available for a binary treatment. The traditional LATE approach assumes the monotonicity condition stating that there are no defiers (or compliers). Since this condition is not always obv
This paper discusses the problem of estimation and inference on the effects of time-varying treatment. We propose a method for inference on the effects treatment histories, introducing a dynamic covariate balancing method combined with penalized regr
In this paper we derive locally D-optimal designs for discrete choice experiments based on multinomial probit models. These models include several discrete explanatory variables as well as a quantitative one. The commonly used multinomial logit model
In unit root testing, a piecewise locally stationary process is adopted to accommodate nonstationary errors that can have both smooth and abrupt changes in second- or higher-order properties. Under this framework, the limiting null distributions of t
This paper presents a simple method for carrying out inference in a wide variety of possibly nonlinear IV models under weak assumptions. The method is non-asymptotic in the sense that it provides a finite sample bound on the difference between the tr