Results 11 to 20 of about 98,771 (295)

CATE Meets ML - Conditional Average Treatment Effect and Machine Learning [PDF]

SSRN Electronic Journal, 2021
AbstractFor treatment effects—one of the core issues in modern econometric analysis—prediction and estimation are two sides of the same coin. As it turns out, machine learning methods are the tool for generalized prediction models. Combined with econometric theory, they allow us to estimate not only the average but a personalized treatment effect—the ...
Jacob, Daniel
core   +6 more sources

Flexible Machine Learning Estimation of Conditional Average Treatment Effects: A Blessing and a Curse [PDF]

Epidemiology, 2023
Causal inference from observational data requires untestable identification assumptions. If these assumptions apply, machine learning methods can be used to study complex forms of causal effect heterogeneity. Recently, several machine learning methods were developed to estimate the conditional average treatment effect (ATE).
Post, Richard A.J., Petkovic, Marko, van den Heuvel, Isabel L., van den Heuvel, Edwin R.   +3 more
openaire   +5 more sources

Estimation of the Local Conditional Tail Average Treatment Effect

Journal of Business & Economic Statistics
The conditional tail average treatment effect (CTATE) is defined as a difference between the conditional tail expectations (CTE’s) of potential outcomes, which can capture heterogeneity and deliver aggregated local information on treatment effects over different quantile levels and is closely related to the notion of second-order stochastic dominance ...
Chen, Le-Yu, Yen, Yu-Min
openaire   +4 more sources

Bounds on the conditional and average treatment effect with unobserved confounding factors

The Annals of Statistics, 2022
For observational studies, we study the sensitivity of causal inference when treatment assignments may depend on unobserved confounders. We develop a loss minimization approach for estimating bounds on the conditional average treatment effect (CATE) when unobserved confounders have a bounded effect on the odds ratio of treatment selection. Our approach
Yadlowsky, Steve, Namkoong, Hongseok, Basu, Sanjay, Duchi, John, Tian, Lu   +4 more
openaire   +3 more sources

Heterogeneous interventional effects with multiple mediators: Semiparametric and nonparametric approaches

Journal of Causal Inference, 2023
We propose semiparametric and nonparametric methods to estimate conditional interventional indirect effects in the setting of two discrete mediators whose causal ordering is unknown. Average interventional indirect effects have been shown to decompose an
Rubinstein Max, Branson Zach, Kennedy Edward H.   +2 more
doaj   +1 more source

Proximal Causal Learning of Conditional Average Treatment Effects

, 2023
Efficiently and flexibly estimating treatment effect heterogeneity is an important task in a wide variety of settings ranging from medicine to marketing, and there are a considerable number of promising conditional average treatment effect estimators currently available.
Erik Sverdrup, Yifan Cui 0001
openaire   +3 more sources

Robust Inference of Conditional Average Treatment Effects Using Dimension Reduction

Statistica Sinica, 2023
It is important to make robust inference of the conditional average treatment effect from observational data, but this becomes challenging when the confounder is multivariate or high-dimensional. In this article, we propose a double dimension reduction method, which reduces the curse of dimensionality as much as possible while keeping the nonparametric
Huang, Ming-Yueh, Yang, Shu
openaire   +4 more sources

Nonparametric tests of conditional treatment effects [PDF]

, 2009
We develop a general class of nonparametric tests for treatment effects conditional on covariates. We consider a wide spectrum of null and alternative hypotheses regarding conditional treatment effects, including (i) the null hypothesis of the ...
Whang, Yoon-Jae, Whang, Y.-J., Yoon-Jae Whang, Lee, S., Lee, Sokbae, Sokbae (Simon) Lee   +5 more
core   +1 more source

Machine learning-based causal models for predicting the response of individual patients to dexamethasone treatment as prophylactic antiemetic

Scientific Reports, 2023
Risk-based strategies are widely used for decision making in the prophylaxis of postoperative nausea and vomiting (PONV), a major complication of general anesthesia.
Taisuke Mizuguchi, Shigehito Sawamura
doaj   +1 more source

Estimating the equity impacts of the smoking ban in England on cotinine levels: a regression discontinuity design

BMJ Open, 2021
Objective To estimate the equity impacts of the 2007 smoking ban in England, for both smokers and non-smokers.Design Doubly robust regression discontinuity analysis of salivary cotinine levels.
Tim Doran, Matthew Robson, Joseph Lord
doaj   +1 more source