David Childers

Assistant Professor of Economics, Carnegie Mellon University

Senior Data Scientist, Abridge

About

In my research I combine approaches from Econometrics, Machine Learning, and High-dimensional Statistics to devise performant and theoretically sound methods for computation, estimation, and decision-making in structured, high-dimensional, dynamic environments, with applications to macroeconomics and causal inference.

Interests

Econometrics
Machine Learning
Macroeconomics
Computational Economics

Education

PhD in Economics, 2016

Yale University

Research

Shantanu Gupta, David Childers, Zachary C. Lipton (2023). Local Causal Discovery for Estimating Causal Effects. CLeaR 2023.

Preprint Code

David Childers, Jesús Fernández-Villaverde, Jesse Perla, Christopher Rackauckas, Peifan Wu (2022). Differentiable State Space Models and Hamiltonian Monte Carlo Estimation. Submitted.

PDF Code NBER WP30573

David Childers (2022). Automated Solution of Heterogeneous Agent Models.

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Shantanu Gupta, Zachary C. Lipton, David Childers (2021). Efficient Online Estimation of Causal Effects by Deciding What to Observe. NeuRIPS 2021.

Preprint PDF Code

Shantanu Gupta, Zachary C. Lipton, David Childers (2021). Estimating Treatment Effects with Observed Confounders and Mediators. UAI 2021.

Preprint

David Childers (2018). Solution of Rational Expectations Models with Function Valued States. R&R at Econometrica.

PDF Code

David Childers (2016). Computational Methods for Economic Models with Function Valued States. PhD Thesis.

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