Using Machine Learning Methods to Support Causal Inference in Econometrics
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Publication:5015913
DOI10.1007/978-3-030-49728-6_2OpenAlexW3011060401MaRDI QIDQ5015913
Christopher Aitken, Mark E. Schaffer, Achim Ahrens
Publication date: 10 December 2021
Published in: Studies in Computational Intelligence (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/978-3-030-49728-6_2
Cites Work
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- Information and asymptotic efficiency in parametric-nonparametric models
- On cross-validated Lasso in high dimensions
- A survey of cross-validation procedures for model selection
- Self-normalized Cramér-type large deviations for independent random variables.
- On the application of probability theory to agricultural experiments. Essay on principles. Section 9. Translated from the Polish and edited by D. M. Dąbrowska and T. P. Speed
- Weak convergence and empirical processes. With applications to statistics
- Least squares after model selection in high-dimensional sparse models
- Simultaneous analysis of Lasso and Dantzig selector
- \(\ell_1\)-penalized quantile regression in high-dimensional sparse models
- Sparse Models and Methods for Optimal Instruments With an Application to Eminent Domain
- VIOLATING IGNORABILITY OF TREATMENT BY CONTROLLING FOR TOO MANY FACTORS
- The cross-validated adaptive epsilon-net estimator
- Asymptotic Statistics
- Estimation of Regression Coefficients When Some Regressors Are Not Always Observed
- On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects
- Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
- Inference on Treatment Effects after Selection among High-Dimensional Controls
- Program Evaluation and Causal Inference With High-Dimensional Data
- Approximate Residual Balancing: Debiased Inference of Average Treatment Effects in High Dimensions
- Deep Neural Networks for Estimation and Inference
- Double/debiased machine learning for treatment and structural parameters
- Robust estimation of causal effects via a high-dimensional covariate balancing propensity score
- Measuring Group Differences in High‐Dimensional Choices: Method and Application to Congressional Speech
- A Generalization of Sampling Without Replacement From a Finite Universe
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