1 citations · 1 across the 4 of their papers we have counts for
5 papers
Integer Programming for Generalized Causal Bootstrap Designs
Jennifer Brennan, Sébastien Lahaie, Adel Javanmard +2
In experimental causal inference, we distinguish between two sources of uncertainty: design uncertainty, due to the treatment assignment mechanism, and sampling uncertainty, when t…
Supergeo Design: Generalized Matching for Geographic Experiments
Aiyou Chen, Nick Doudchenko, Shunhua Jiang +2
We propose a generalization of the standard matched pairs design in which experimental units (often geographic regions or geos) may be combined into larger units/regions called "su…
From Quantum Codes to Gravity: A Journey of Gravitizing Quantum Mechanics
ChunJun Cao
In this note, I review a recent approach to quantum gravity that "gravitizes" quantum mechanics by emerging geometry and gravity from complex quantum states. Drawing further insigh…
Causal inference with bipartite designs: A generalized propensity score approach
Nick Doudchenko, Minzhengxiong Zhang, Evgeni Drynkin +5
Bipartite experiments, in which one set of units receives a treatment while outcomes are measured on another set, have recently garnered attention for their ability to capture inte…
Estimation of Discrete Choice Models: A Machine Learning Approach
Nick Doudchenko, Evgeni Drynkin
In this paper we propose a new method of estimation for discrete choice demand models when individual level data are available. The method employs a two-step procedure. Step 1 pred…