• Corpus ID: 244896263

Dimension-Free Average Treatment Effect Inference with Deep Neural Networks

@article{Du2021DimensionFreeAT,
  title={Dimension-Free Average Treatment Effect Inference with Deep Neural Networks},
  author={Xinze Du and Yingying Fan and Jinchi Lv and Tianshu Sun and Patrick Vossler},
  journal={ArXiv},
  year={2021},
  volume={abs/2112.01574}
}
This paper investigates the estimation and inference of the average treatment effect (ATE) using deep neural networks (DNNs) in the potential outcomes framework. Under some regularity conditions, the observed response can be formulated as the response of a mean regression problem with both the confounding variables and the treatment indicator as the independent variables. Using such formulation, we investigate two methods for ATE estimation and inference based on the estimated mean regression… 

References

SHOWING 1-10 OF 22 REFERENCES
Causal Effect Inference with Deep Latent-Variable Models
TLDR
This work builds on recent advances in latent variable modeling to simultaneously estimate the unknown latent space summarizing the confounders and the causal effect and shows its method is significantly more robust than existing methods, and matches the state-of-the-art on previous benchmarks focused on individual treatment effects.
Program evaluation and causal inference with high-dimensional data
TLDR
This paper shows that a key ingredient enabling honest inference is the use of orthogonal or doubly robust moment conditions in estimating certain reduced form functional parameters, and provides results on honest inference for (function-valued) parameters within this general framework where any high-quality, modern machine learning methods can be used to learn the nonparametric/high-dimensional components of the model.
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.
Estimating individual treatment effect: generalization bounds and algorithms
TLDR
A novel, simple and intuitive generalization-error bound is given showing that the expected ITE estimation error of a representation is bounded by a sum of the standard generalized-error of that representation and the distance between the treated and control distributions induced by the representation.
Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
  • Stefan Wager, S. Athey
  • Mathematics, Computer Science
    Journal of the American Statistical Association
  • 2018
TLDR
This is the first set of results that allows any type of random forest, including classification and regression forests, to be used for provably valid statistical inference and is found to be substantially more powerful than classical methods based on nearest-neighbor matching.
Improving Covariate Balancing Propensity Score : A Doubly Robust and Efficient Approach ∗
Inverse probability of treatment weighting (IPTW) is a popular method for estimating causal effects in many disciplines. However, empirical studies show that the IPTW estimators can be sensitive to
Semiparametric instrumental variable estimation of treatment response models
Machine Learning and Causal Inference for Policy Evaluation
TLDR
This talk will review several recent papers that attempt to bring the tools of supervised machine learning to bear on the problem of policy evaluation, and propose to divide the features of a model into causal features, whose values may be manipulated in a counterfactual policy environment, and attributes.
Doubly robust estimation of causal effects.
TLDR
The authors present a conceptual overview of doubly robust estimation, a simple worked example, results from a simulation study examining performance of estimated and bootstrapped standard errors, and a discussion of the potential advantages and limitations of this method.
On deep learning as a remedy for the curse of dimensionality in nonparametric regression
TLDR
It is shown that least squares estimates based on multilayer feedforward neural networks are able to circumvent the curse of dimensionality in nonparametric regression.
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