Sebastian Galiani, Patrick J. McEwan, and Brian Quistorff (2017) External and Internal Validity of a Geographic Quasi-Experiment Embedded in a Cluster-Randomized Experiment, in Matias D. Cattaneo, Juan Carlos Escanciano (ed.) Regression Discontinuity Designs (Advances in Econometrics, Volume 38) Emerald Publishing Limited, pp. 195-236.
Sebastian Galiani and Brian Quistorff (2017) The synth_runner Package: Utilities to Automate Synthetic Control Estimation Using synth, Stata Journal, 17(4), pp. 834-849. Code.
Matt Goldman and Brian Quistorff (2018) Pricing Engine: Estimating Causal Impacts in Real World Business Settings (updated), Microsoft Journal of Applied Research, 9, pp. 10-18.
George G. Vega Yon and Brian Quistorff (2019) parallel: A command for parallel computing, Stata Journal, 19(3), pp. 667-684. Code.
Brian Quistorff, Matt Goldman, and Jason Thorpe (2020) Sparse Synthetic Controls: Unit-Level Counterfactuals from High-Dimensional Data, Microsoft Journal of Applied Research, 14, pp.155-170.
Fengshi Niu, Harsha Nori, Brian Quistorff, Rich Caruana, Donald Ngwe, and Aadharsh Kannan (2022) Differentially Private Estimation of Heterogeneous Causal Effects, CLeaR (Causal Learning and Reasoning) 2022.
Raymond Guiteras, Ahnjeong Kim, Brian Quistorff, and Clayson Shumway (2023) statacons: An SCons-Based Build Tool for Stata, Stata Journal 23(1). GitHub, OSF site.
John Meszaros and Brian Quistorff (2023) Right-to-work revisited, Industrial Relations.
Sebastian Galiani and Brian Quistorff (2024) Assessing External Validity in Practice, Research in Economics 78(3).
"Expanding the Frontier of Economic Statistics Using Big Data: A Case Study of Regional Employment," with Abe C. Dunn, Eric English, Kyle K. Hood, and Lowell Mason.
"Causal Grid: Regular partitions for subgroup identification and analysis"
"ML for Experimental Design," with Gentry Johnson
"Right-to-Work Revisited," with John Meszaros
"Matching on What Matters: A Metric Learning Approach to Matching Estimation with Many Covariates," with Gentry Johnson and Matt Goldman
"The Effects of Energy Prices on Manufacturing Employment"
"Capitalitis? Effects of the 1960 Brazilian Capital Relocation"
"Credit Constraints, Discounting and Investment in Health: Evidence from Micropayments for Clean Water in Dhaka," with Raymond Guiteras, David I. Levine, and Thomas Polley
"Inferring Product Hierarchies using ML" with Gentry Johnson
Abstract: When estimating high-dimensional demand systems, it is essential to limit the complexity of cross-product substitution patterns to avoid the curse of dimensionality. One common way is to use a product hierarchy and only allow interactions between neighboring elements in the tree as in the Almost Ideal Demand (AID) system and nested logit models. A common challenge of using these models is that hierarchies are not known a priori, especially in cases with many products. We develop a method to infer such hierarchies from the data using standard Machine Learning (ML) tools.