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Found 17 papers, 1 papers with code
Partially identified heteroskedastic SVARs
This paper studies the identification of Structural Vector Autoregressions (SVARs) exploiting a break in the variances of the structural shocks.
Adaptive Experimental Design for Policy Learning
Setting the worst-case expected regret as the performance criterion of adaptive sampling and recommended policies, we derive its asymptotic lower bounds, and propose a strategy, Adaptive Sampling-Policy Learning strategy (PLAS), whose leading factor of the regret upper bound aligns with the lower bound as the size of experimental units increases.
Treatment Choice, Mean Square Regret and Partial Identification
We consider a decision maker who faces a binary treatment choice when their welfare is only partially identified from data.
Individualized Treatment Allocation in Sequential Network Games
Focusing on sequential decision games of interacting agents, this paper develops a method to obtain optimal treatment assignment rules that maximize a social welfare criterion by evaluating stationary distributions of outcomes.
Asymptotically Optimal Fixed-Budget Best Arm Identification with Variance-Dependent Bounds
We evaluate the decision based on the expected simple regret, which is the difference between the expected outcomes of the best arm and the recommended arm.
Stochastic Treatment Choice with Empirical Welfare Updating
We show analytically a welfare-optimal way of updating the prior using empirical welfare; this posterior is not feasible to compute, so we propose a variational Bayes approximation for the optimal posterior.
Best Arm Identification with Contextual Information under a Small Gap
We then develop the ``Random Sampling (RS)-Augmented Inverse Probability weighting (AIPW) strategy,'' which is asymptotically optimal in the sense that the probability of misidentification under the strategy matches the lower bound when the budget goes to infinity in the small-gap regime.
Treatment Choice with Nonlinear Regret
The literature focuses on the mean of welfare regret, which can lead to undesirable treatment choice due to sensitivity to sampling uncertainty.
Policy Choice in Time Series by Empirical Welfare Maximization
This paper develops a novel method for policy choice in a dynamic setting where the available data is a multivariate time series.
von Mises-Fisher distributions and their statistical divergence
The von Mises-Fisher family is a parametric family of distributions on the surface of the unit ball, summarised by a concentration parameter and a mean direction.
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.
Testing Instrument Validity with Covariates
In Monte Carlo exercises we find gains in finite sample power from testing restrictions jointly and distillation.
Evidence Aggregation for Treatment Choice
Consider a planner who has to decide whether or not to introduce a new policy to a certain local population.
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.
Identification and Inference Under Narrative Restrictions
We consider structural vector autoregressions subject to 'narrative restrictions', which are inequality restrictions on functions of the structural shocks in specific periods.
A note on global identification in structural vector autoregressions
In a landmark contribution to the structural vector autoregression (SVARs) literature, Rubio-Ramirez, Waggoner, and Zha (2010, `Structural Vector Autoregressions: Theory of Identification and Algorithms for Inference,' Review of Economic Studies) shows a necessary and sufficient condition for equality restrictions to globally identify the structural parameters of a SVAR.
Who Should Get Vaccinated? Individualized Allocation of Vaccines Over SIR Network
How to allocate vaccines over heterogeneous individuals is one of the important policy decisions in pandemic times.
