• Corpus ID: 220363647

Cross-Fitting and Averaging for Machine Learning Estimation of Heterogeneous Treatment Effects

@article{Jacob2020CrossFittingAA,
  title={Cross-Fitting and Averaging for Machine Learning Estimation of Heterogeneous Treatment Effects},
  author={Daniela Jacob},
  journal={arXiv: Methodology},
  year={2020}
}
  • D. Jacob
  • Published 6 July 2020
  • Computer Science
  • arXiv: Methodology
We investigate the finite sample performance of sample splitting, cross-fitting and averaging for the estimation of the conditional average treatment effect. Recently proposed methods, so-called meta-learners, make use of machine learning to estimate different nuisance functions and hence allow for fewer restrictions on the underlying structure of the data. To limit a potential overfitting bias that may result when using machine learning methods, cross-fitting estimators have been proposed… 

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References

SHOWING 1-10 OF 22 REFERENCES

Double/Debiased Machine Learning for Treatment and Structural Parameters

TLDR
This work revisits the classic semiparametric problem of inference on a low dimensional parameter θ_0 in the presence of high-dimensional nuisance parameters η_0 and proves that DML delivers point estimators that concentrate in a N^(-1/2)-neighborhood of the true parameter values and are approximately unbiased and normally distributed, which allows construction of valid confidence statements.

Optimal doubly robust estimation of heterogeneous causal effects

TLDR
A two-stage doubly robust CATE estimator is studied and a generic model-free error bound is given and it is shown that this estimator can be oracle efficient under even weaker conditions, if used with a specialized form of sample splitting and careful choices of tuning parameters.

Quasi-oracle estimation of heterogeneous treatment effects

TLDR
This paper develops a general class of two-step algorithms for heterogeneous treatment effect estimation in observational studies that have a quasi-oracle property, and implements variants of this approach based on penalized regression, kernel ridge regression, and boosting, and find promising performance relative to existing baselines.

Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence

TLDR
An Empirical Monte Carlo Study that relies on arguably realistic data generation processes (DGPs) based on actual data to investigate the finite sample performance of causal machine learning estimators for heterogeneous causal effects at different aggregation levels.

Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects

TLDR
The Bayesian causal forest model permits treatment effect heterogeneity to be regularized separately from the prognostic effect of control variables, making it possible to informatively "shrink to homogeneity".

Machine Learning for Causal Inference: On the Use of Cross-fit Estimators

TLDR
Doubly robust estimators with ensemble learning and cross-fitting may be the preferred approach for estimation of the average causal effect in most epidemiologic studies, however, these approaches may require larger sample sizes to avoid finite-sample issues.

Nonparametric estimation of causal heterogeneity under high-dimensional confounding

This paper considers the practically important case of nonparametrically estimating heterogeneous average treatment effects that vary with a limited number of discrete and continuous covariates in a

Estimation of Conditional Average Treatment Effects With High-Dimensional Data

Abstract Given the unconfoundedness assumption, we propose new nonparametric estimators for the reduced dimensional conditional average treatment effect (CATE) function. In the first stage, the

On Asymptotically Efficient Estimation in Semiparametric Models

On presente une methode generale qui ameliore et modifie la construction de Bickel (1982) des estimateurs adaptatifs