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Nick Doudchenko

Nick Doudchenko

I'm interested in all things causal inference, economics and optimization, but my research is primarily focused on experimental design. I completed a PhD in Economics at Stanford Graduate School of Business and an MS in Statistics at Stanford in 2018. Before that I received an MA in Economics from New Economic School in Moscow and a BS in Mathematics from Moscow State University.
Authored Publications
Google Publications
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    Synthetic Design: An Optimization Approach to Experimental Design with Synthetic Controls
    Guido Imbens
    Jann Spiess
    Khashayar Khosravi
    Miles Lubin
    35th Conference on Neural Information Processing Systems (NeurIPS 2021) (2021)
    Preview abstract We investigate the optimal design of experimental studies that have pre-treatment outcome data available. The average treatment effect is estimated as the difference between the weighted average outcomes of the treated and control units. A number of commonly used approaches fit this formulation, including the difference-in-means estimator and a variety of synthetic-control techniques. We propose several methods for choosing the set of treated units in conjunction with the weights. Observing the NP-hardness of the problem, we introduce a mixed-integer programming formulation which selects both the treatment and control sets and unit weightings. We prove that these proposed approaches lead to qualitatively different experimental units being selected for treatment. We use simulations based on publicly available data from the US Bureau of Labor Statistics that show improvements in terms of mean squared error and statistical power when compared to simple and commonly used alternatives such as randomized trials. View details
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