Double machine learning for sample selection models
- URL: http://arxiv.org/abs/2012.00745v3
- Date: Sat, 1 May 2021 09:12:02 GMT
- Title: Double machine learning for sample selection models
- Authors: Michela Bia, Martin Huber, Luk\'a\v{s} Laff\'ers
- Abstract summary: This paper considers the evaluation of discretely distributed treatments when outcomes are only observed for a subpopulation due to sample selection or outcome attrition.
We make use of (a) Neyman-orthogonal, doubly robust, and efficient score functions, which imply the robustness of treatment effect estimation to moderate regularization biases in the machine learning-based estimation of the outcome, treatment, or sample selection models and (b) sample splitting (or cross-fitting) to prevent overfitting bias.
- Score: 0.12891210250935145
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper considers the evaluation of discretely distributed treatments when
outcomes are only observed for a subpopulation due to sample selection or
outcome attrition. For identification, we combine a selection-on-observables
assumption for treatment assignment with either selection-on-observables or
instrumental variable assumptions concerning the outcome attrition/sample
selection process. We also consider dynamic confounding, meaning that
covariates that jointly affect sample selection and the outcome may (at least
partly) be influenced by the treatment. To control in a data-driven way for a
potentially high dimensional set of pre- and/or post-treatment covariates, we
adapt the double machine learning framework for treatment evaluation to sample
selection problems. We make use of (a) Neyman-orthogonal, doubly robust, and
efficient score functions, which imply the robustness of treatment effect
estimation to moderate regularization biases in the machine learning-based
estimation of the outcome, treatment, or sample selection models and (b) sample
splitting (or cross-fitting) to prevent overfitting bias. We demonstrate that
the proposed estimators are asymptotically normal and root-n consistent under
specific regularity conditions concerning the machine learners and investigate
their finite sample properties in a simulation study. We also apply our
proposed methodology to the Job Corps data for evaluating the effect of
training on hourly wages which are only observed conditional on employment. The
estimator is available in the causalweight package for the statistical software
R.
Related papers
- Improving Bias Correction Standards by Quantifying its Effects on Treatment Outcomes [54.18828236350544]
Propensity score matching (PSM) addresses selection biases by selecting comparable populations for analysis.
Different matching methods can produce significantly different Average Treatment Effects (ATE) for the same task, even when meeting all validation criteria.
To address this issue, we introduce a novel metric, A2A, to reduce the number of valid matches.
arXiv Detail & Related papers (2024-07-20T12:42:24Z) - Estimating treatment effects from single-arm trials via latent-variable
modeling [14.083487062917085]
Single-arm trials, where all patients belong to the treatment group, can be a viable alternative but require access to an external control group.
We propose an identifiable deep latent-variable model for this scenario.
Our results show improved performance both for direct treatment effect estimation as well as for effect estimation via patient matching.
arXiv Detail & Related papers (2023-11-06T10:12:54Z) - Doubly Robust Estimation of Direct and Indirect Quantile Treatment
Effects with Machine Learning [0.0]
We suggest a machine learning estimator of direct and indirect quantile treatment effects under a selection-on-observables assumption.
The proposed method is based on the efficient score functions of the cumulative distribution functions of potential outcomes.
We also propose a multiplier bootstrap for statistical inference and show the validity of the multiplier.
arXiv Detail & Related papers (2023-07-03T14:27:15Z) - TCFimt: Temporal Counterfactual Forecasting from Individual Multiple
Treatment Perspective [50.675845725806724]
We propose a comprehensive framework of temporal counterfactual forecasting from an individual multiple treatment perspective (TCFimt)
TCFimt constructs adversarial tasks in a seq2seq framework to alleviate selection and time-varying bias and designs a contrastive learning-based block to decouple a mixed treatment effect into separated main treatment effects and causal interactions.
The proposed method shows satisfactory performance in predicting future outcomes with specific treatments and in choosing optimal treatment type and timing than state-of-the-art methods.
arXiv Detail & Related papers (2022-12-17T15:01:05Z) - Sample Constrained Treatment Effect Estimation [28.156207324508706]
We focus on designing efficient randomized controlled trials, to accurately estimate the effect of some treatment on a population of $n$ individuals.
In particular, we study sample-constrained treatment effect estimation, where we must select a subset of $s ll n$ individuals from the population to experiment on.
arXiv Detail & Related papers (2022-10-12T21:13:47Z) - Avoiding Biased Clinical Machine Learning Model Performance Estimates in
the Presence of Label Selection [3.3944964838781093]
We describe three classes of label selection and simulate five causally distinct scenarios to assess how particular selection mechanisms bias a suite of commonly reported binary machine learning model performance metrics.
We find that naive estimates of AUROC on the observed population undershoot actual performance by up to 20%.
Such a disparity could be large enough to lead to the wrongful termination of a successful clinical decision support tool.
arXiv Detail & Related papers (2022-09-15T22:30:14Z) - Continuous-Time Modeling of Counterfactual Outcomes Using Neural
Controlled Differential Equations [84.42837346400151]
Estimating counterfactual outcomes over time has the potential to unlock personalized healthcare.
Existing causal inference approaches consider regular, discrete-time intervals between observations and treatment decisions.
We propose a controllable simulation environment based on a model of tumor growth for a range of scenarios.
arXiv Detail & Related papers (2022-06-16T17:15:15Z) - Robust and Agnostic Learning of Conditional Distributional Treatment
Effects [62.44901952244514]
The conditional average treatment effect (CATE) is the best point prediction of individual causal effects.
In aggregate analyses, this is usually addressed by measuring distributional treatment effect (DTE)
We provide a new robust and model-agnostic methodology for learning the conditional DTE (CDTE) for a wide class of problems.
arXiv Detail & Related papers (2022-05-23T17:40:31Z) - Evaluating (weighted) dynamic treatment effects by double machine
learning [0.12891210250935145]
We consider evaluating the causal effects of dynamic treatments in a data-driven way under a selection-on-observables assumption.
We make use of so-called Neyman-orthogonal score functions, which imply the robustness of treatment effect estimation to moderate (local) misspecifications.
We demonstrate that the estimators are regularityally normal and $sqrtn$-consistent under specific conditions.
arXiv Detail & Related papers (2020-12-01T09:55:40Z) - Enabling Counterfactual Survival Analysis with Balanced Representations [64.17342727357618]
Survival data are frequently encountered across diverse medical applications, i.e., drug development, risk profiling, and clinical trials.
We propose a theoretically grounded unified framework for counterfactual inference applicable to survival outcomes.
arXiv Detail & Related papers (2020-06-14T01:15:00Z) - Generalization Bounds and Representation Learning for Estimation of
Potential Outcomes and Causal Effects [61.03579766573421]
We study estimation of individual-level causal effects, such as a single patient's response to alternative medication.
We devise representation learning algorithms that minimize our bound, by regularizing the representation's induced treatment group distance.
We extend these algorithms to simultaneously learn a weighted representation to further reduce treatment group distances.
arXiv Detail & Related papers (2020-01-21T10:16:33Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.