Using machine learning to individualize treatment effect estimation: Challenges and opportunities
A Curth, RW Peck, E McKinney… - Clinical …, 2024 - Wiley Online Library
The use of data from randomized clinical trials to justify treatment decisions for real‐world
patients is the current state of the art. It relies on the assumption that average treatment …
patients is the current state of the art. It relies on the assumption that average treatment …
The predictive approaches to treatment effect heterogeneity (PATH) statement
DM Kent, JK Paulus, D Van Klaveren… - Annals of internal …, 2020 - acpjournals.org
Heterogeneity of treatment effect (HTE) refers to the nonrandom variation in the magnitude
or direction of a treatment effect across levels of a covariate, as measured on a selected …
or direction of a treatment effect across levels of a covariate, as measured on a selected …
Estimating individual treatment effects using non-parametric regression models: A review
Large observational data are increasingly available in disciplines such as health, economic
and social sciences, where researchers are interested in causal questions rather than …
and social sciences, where researchers are interested in causal questions rather than …
Machine learning estimation of heterogeneous causal effects: Empirical Monte Carlo evidence
We investigate the finite-sample performance of causal machine learning estimators for
heterogeneous causal effects at different aggregation levels. We employ an empirical Monte …
heterogeneous causal effects at different aggregation levels. We employ an empirical Monte …
Learning counterfactual representations for estimating individual dose-response curves
Estimating what would be an individual's potential response to varying levels of exposure to
a treatment is of high practical relevance for several important fields, such as healthcare …
a treatment is of high practical relevance for several important fields, such as healthcare …
In search of insights, not magic bullets: Towards demystification of the model selection dilemma in heterogeneous treatment effect estimation
A Curth, M Van Der Schaar - International Conference on …, 2023 - proceedings.mlr.press
Personalized treatment effect estimates are often of interest in high-stakes applications–
thus, before deploying a model estimating such effects in practice, one needs to be sure that …
thus, before deploying a model estimating such effects in practice, one needs to be sure that …
Perfect match: A simple method for learning representations for counterfactual inference with neural networks
Learning representations for counterfactual inference from observational data is of high
practical relevance for many domains, such as healthcare, public policy and economics …
practical relevance for many domains, such as healthcare, public policy and economics …
Causal decision making and causal effect estimation are not the same… and why it matters
C Fernández-Loría, F Provost - INFORMS Journal on Data …, 2022 - pubsonline.informs.org
Causal decision making (CDM) at scale has become a routine part of business, and
increasingly, CDM is based on statistical models and machine learning algorithms …
increasingly, CDM is based on statistical models and machine learning algorithms …
Calibration of heterogeneous treatment effects in randomized experiments
Machine learning is commonly used to estimate the heterogeneous treatment effects (HTEs)
in randomized experiments. Using large-scale randomized experiments on the Facebook …
in randomized experiments. Using large-scale randomized experiments on the Facebook …
Reconsidering generative objectives for counterfactual reasoning
There has been recent interest in exploring generative goals for counterfactual reasoning,
such as individualized treatment effect (ITE) estimation. However, existing solutions often fail …
such as individualized treatment effect (ITE) estimation. However, existing solutions often fail …