David Childers

Visiting Assistant Professor of Economics

Bowdoin College

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.

I love helping people with their econometric questions, and host weekly econometrics office hours, open to anyone in the world, currently Wed 4-6pm Eastern Time US each week or by appointment: details and signup.

Interests

Econometrics
Machine Learning
Macroeconomics
Computational Economics

Education

PhD in Economics, 2016

Yale University

Research

Helen Zhou, Audrey Huang, Kamyar Azizzadenesheli, David Childers, Zachary C. Lipton (2024). Timing as an Action: Learning When to Observe and Act. AISTATS 2024.

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

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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.

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

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David Childers, Keshav Dogra (2019). Perturbation Methods for Incomplete Markets Economies.

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

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