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Estimating Continuous Treatment Effects in Panel Data using Machine Learning with a Climate Application

Sylvia Klosin, Max Vilgalys

arXiv 18 Jul 2022 · Econometrics · 2 citations (OpenAlex)

arXiv:2207.08789 · PDF · DOI · OpenAlex · Extracted main text

Abstract

Economists often estimate continuous treatment effects in panel data using linear two-way fixed effects models (TWFE). When the treatment-outcome relationship is nonlinear, TWFE is misspecifed and potentially biased for the average partial derivative (APD). We develop an automatic double/de-biased machine learning (ADML) estimator that is consistent for the population APD while allowing additive unit fixed effects, nonlinearities, and high dimensional heterogeneity. We prove asymptotic normality and add two refinements - optimization based de-biasing and analytic derivatives - that reduce bias and remove numerical approximation error. Simulations show that the proposed method outperforms high order polynomial OLS and standard ML estimators. Our estimator leads to significantly larger (by 50%), but equally precise, estimates of the effect of extreme heat on corn yield compared to standard linear models.

Citation extraction

42
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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Burke, Marshall, Emerick, Kyle (2016) Adaptation to climate change: Evidence from US agriculture0.874162100%
2Chernozhukov, Victor, Newey, Whitney, Singh, Rahul (2022) Automatic debiased machine learning of causal and structural effects0.85113562%
3Semenova, Vira, Goldman, Matt, Chernozhukov, Victor, Taddy, Matt (2023) Inference on heterogeneous treatment effects in high-dimensional dynamic panels under weak dependence0.81142100%
4Wooldridge, Jeffrey M (2021) Two-way fixed effects, the two-way mundlak regression, and difference-in-differences estimators0.81142100%
5Schlenker, Wolfram, Roberts, Michael J (2009) Nonlinear temperature effects indicate severe damages to U.S. crop yields under climate change.0.64422100%
6Belloni, Alexandre, Chernozhukov, Victor, Hansen, Christian, Kozbur,… (2016) Inference in high-dimensional panel models with an application to gun control0.64422100%
7Hastie, Trevor, Tibshirani, Robert, Friedman, Jerome H, Friedman, Je… (2009) The elements of statistical learning: data mining, inference, and prediction0.64422100%
8Hoderlein, Stefan, White, Halbert (2012) Nonparametric identification in nonseparable panel data models with generalized fixed effects0.58531100%
9Klosin, Sylvia (2021) Automatic Double Machine Learning for Continuous Treatment Effects self0.5113233%
10Chartrand, Rick (2017) Numerical differentiation of noisy, nonsmooth, multidimensional data0.5112250%

Showing the top 10 of 42 scored citations.

Cited by, within the corpus

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Citing paperIntensityMentionsSections
1Minimax Semiparametric Learning With Approximate Sparsity0.40511
2Arellano-Bond LASSO Estimator for Dynamic Linear Panel Models$^*$0.40511
3Weak instrumental variables due to nonlinearities in panel data: A Super Learner Control Function estimator0.40511
4An Introduction to Double/Debiased Machine Learning0.40511
5xtdml: Double Machine Learning Estimation to Static Panel Data Models with Fixed Effects in R0.40511