• Corpus ID: 320456

Estimating individual treatment effect: generalization bounds and algorithms

@inproceedings{Shalit2017EstimatingIT,
  title={Estimating individual treatment effect: generalization bounds and algorithms},
  author={Uri Shalit and Fredrik D. Johansson and David A. Sontag},
  booktitle={ICML},
  year={2017}
}
There is intense interest in applying machine learning to problems of causal inference in fields such as healthcare, economics and education. [...] Key Method We give a novel, simple and intuitive generalization-error bound showing that the expected ITE estimation error of a representation is bounded by a sum of the standard generalization-error of that representation and the distance between the treated and control distributions induced by the representation.Expand
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References

SHOWING 1-10 OF 91 REFERENCES
Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
  • Stefan Wager, S. Athey
  • Computer Science, Mathematics
    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.
Double machine learning for treatment and causal parameters
Most modern supervised statistical/machine learning (ML) methods are explicitly designed to solve prediction problems very well. Achieving this goal does not imply that these methods automatically
Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks
TLDR
Empirical results on real-world data indicate that certain methods are indeed able to distinguish cause from effect using only purely observational data, although more benchmark data would be needed to obtain statistically significant conclusions.
Inference on Treatment Effects after Selection Amongst High-Dimensional Controls
TLDR
This work develops a novel estimation and uniformly valid inference method for the treatment effect in this setting, called the "post-double-selection" method, which resolves the problem of uniform inference after model selection for a large, interesting class of models.
Approximate Residual Balancing: De-Biased Inference of Average Treatment Effects in High Dimensions.
There are many settings where researchers are interested in estimating average treatment effects and are willing to rely on the unconfoundedness assumption, which requires that the treatment
Bounds on direct effects in the presence of confounded intermediate variables.
TLDR
The symbolic Balke-Pearl linear programming method is applied to derive closed-form formulas for the upper and lower bounds on the ACDE under various assumptions of monotonicity to enable clinical experimenters to assess the direct effect of treatment from observed data with minimum computational effort.
Efficient Inference of Average Treatment Effects in High Dimensions via Approximate Residual Balancing
There are many settings where researchers are interested in estimating average treatment effects and are willing to rely on the unconfoundedness assumption, which requires that the treatment
A Nonparametric Bayesian Analysis of Heterogenous Treatment Effects in Digital Experimentation
TLDR
A fast and scalable Bayesian nonparametric analysis of heterogenous treatment effects and their measurement in relation to observable covariates and it is argued that practitioners should look to ensembles of trees (forests) rather than individual trees in their analysis.
Bayesian Nonparametric Modeling for Causal Inference
Researchers have long struggled to identify causal effects in nonexperimental settings. Many recently proposed strategies assume ignorability of the treatment assignment mechanism and require fitting
Recursive partitioning for heterogeneous causal effects
  • S. Athey, G. Imbens
  • Mathematics, Economics
    Proceedings of the National Academy of Sciences
  • 2016
TLDR
This paper provides a data-driven approach to partition the data into subpopulations that differ in the magnitude of their treatment effects, and proposes an “honest” approach to estimation, whereby one sample is used to construct the partition and another to estimate treatment effects for each subpopulation.
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