Evan Munro (@_evan_munro) profile picture

Evan Munro

@_evan_munro

Assistant Professor @ChicagoBooth; econometrics, causal inference.

 Chicago
evanmunro.ca
Joined March 2018
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I'm excited to share a career update! After a year as a Neyman Visiting Assistant Professor @UCBStatistics I'll be joining @ChicagoBooth in July 2025 as an Assistant Professor of Econometrics and Statistics.
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I'm very excited to be organizing an invited session at IMS 2022 in London on Inference Methods for Adaptively Collected Data!! The speakers are @nathankallus, Koulik Khamaru, @_evan_munro, and myself, and @SusanMurphylab1 will chair the session!! Happening on June 29, 1:30-3:30
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Hey @NeruIPS2021, come join us on Monday (Dec 13th) for the Machine Learning meets Econometrics (MLECON) workshop! neurips.cc/virtual/2021/work…
Image from Limor Gultchin (@gultchin)
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Excited to share a new working paper with S. Wager and @xu_kuang on estimating treatment effects in a market equilibrium! The paper applies a mix of game theory, asymptotic theory, and experiment design to tackle a problem in causal inference. arxiv.org/pdf/2109.11647.pdf (1/3).
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Programs are often evaluated within a market economy, where individuals buy and sell at the prevailing market price. Equilibrium prices introduce interference, so usual estimators of the ATE do not correctly estimate the average total effect of a treatment. (2/3)
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We provide estimators for the average direct and indirect treatment effects based on an augmented unit-level randomized experiment. We provide methods for asymptotically exact inference, and describe how equilibrium interference impacts the variance of the estimators. (3/3)
My paper "Latent Dirichlet Analysis of Categorical Survey Responses", with Serena Ng, is now forthcoming at JBES! doi.org/10.1080/07350015.202… The main contribution is providing a methodology for summarizing and interpreting heterogeneity in qualitative survey data. (1/4)
Latent Dirichlet Analysis of Categorical Survey Responses

Latent Dirichlet Analysis of Categorical Survey Responses

Beliefs are important determinants of an individual’s choices and economic outcomes, so understanding how they comove and differ across individuals is of considerable interest. Researchers often re...

tandfonline.com
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A key insight in the paper is that an economic model of belief formation leads to a version of LDA for survey data. The structural model underpinning the statistical model guides interpretation, identification and estimation. (3/4)
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The paper also includes some applications of the methodology to analyzing survey data on consumer expectations and returns to education. An R package dhlvm (hosted on my Github) is available for the models in the paper. (4/4)
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New version of our paper, which includes a theoretical model of information acquisition that motivates using LDA for analyzing categorical survey data.
NBER (@nberpubs) profile pictureNBER
@nberpubs25 May 2020
Latent Dirichlet analysis of categorical survey expectations, from Evan M. Munro and Serena Ng nber.org/papers/w27182
Image from NBER (@nberpubs)
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Great experience sharing our research on designing an optimal draft allocation mechanism at the MIT Sports Analytics Conference !
Sloan Sports Conf. (@SloanSportsConf) profile pictureSloan Sports Conf.@SloanSportsConf7 Mar 2020
Research Paper Competition Presented by @MLB Grand Prize: Evan Munro and Martino Bianchio, “Designing a No-Tanking Draft Lottery” Audience-choice: Samuel Kalman and Jonathan Bosch, “NBA Lineup Analysis on Clustered Player Tendencies”
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NBER (@nberpubs) profile picture
NBER
@nberpubs23 Dec 2019
How generative adversarial networks can be used to make Monte Carlo studies more credible by tying simulation designs to real data sets, from @Susan_Athey, Guido W. Imbens, Jonas Metzger, and Evan M. Munro nber.org/papers/w26566
Image from NBER (@nberpubs)
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My first open source contribution: boosted regression forests for GRF!
Julie Tibshirani (@julietibs) profile pictureJulie Tibshirani@julietibs1 Jun 2019
GRF version 0.10.3 is now available on CRAN! This release includes support for sample weighting, merging forests, and boosted regression forests, as well as improvements to memory usage. Full list of changes: github.com/grf-labs/grf/blob…
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