Efficient estimation of weighted cumulative treatment effects by double/debiased machine learning

• URL: http://arxiv.org/abs/2305.02373v1
• Date: Wed, 3 May 2023 18:19:18 GMT
• Title: Efficient estimation of weighted cumulative treatment effects by double/debiased machine learning
• Authors: Shenbo Xu and Bang Zheng and Bowen Su and Stan Finkelstein and Roy Welsch and Kenney Ng and Ioanna Tzoulaki and Zach Shahn
• Abstract summary: We propose a class of one-step cross-fitted double/debiased machine learning estimators for the weighted cumulative causal effect. We apply the proposed methods to real-world observational data from a UK primary care database to compare the effects of anti-diabetic drugs on cancer outcomes.
• Score: 3.086361225427304
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In empirical studies with time-to-event outcomes, investigators often leverage observational data to conduct causal inference on the effect of exposure when randomized controlled trial data is unavailable. Model misspecification and lack of overlap are common issues in observational studies, and they often lead to inconsistent and inefficient estimators of the average treatment effect. Estimators targeting overlap weighted effects have been proposed to address the challenge of poor overlap, and methods enabling flexible machine learning for nuisance models address model misspecification. However, the approaches that allow machine learning for nuisance models have not been extended to the setting of weighted average treatment effects for time-to-event outcomes when there is poor overlap. In this work, we propose a class of one-step cross-fitted double/debiased machine learning estimators for the weighted cumulative causal effect as a function of restriction time. We prove that the proposed estimators are consistent, asymptotically linear, and reach semiparametric efficiency bounds under regularity conditions. Our simulations show that the proposed estimators using nonparametric machine learning nuisance models perform as well as established methods that require correctly-specified parametric nuisance models, illustrating that our estimators mitigate the need for oracle parametric nuisance models. We apply the proposed methods to real-world observational data from a UK primary care database to compare the effects of anti-diabetic drugs on cancer clinical outcomes.

Related papers

• Non-parametric efficient estimation of marginal structural models with multi-valued time-varying treatments [0.9558392439655012]
  We use machine learning together with recent developments in semi-parametric efficiency theory for longitudinal studies to propose such an estimator. We show conditions under which the proposed estimator is efficient as anally normal, and sequentially doubly robust in the sense that it is consistent if, for each time point, either the outcome or the treatment mechanism is consistently estimated.
  arXiv  Detail & Related papers  (2024-09-27T14:29:12Z)
• Valid causal inference with unobserved confounding in high-dimensional settings [0.0]
  We show how valid semiparametric inference can be obtained in the presence of unobserved confounders and high-dimensional nuisance models. We propose uncertainty intervals which allow for unobserved confounding, and show that the resulting inference is valid when the amount of unobserved confounding is small.
  arXiv  Detail & Related papers  (2024-01-12T13:21:20Z)
• SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data [83.50281440043241]
  We study the problem of inferring heterogeneous treatment effects from time-to-event data. We propose a novel deep learning method for treatment-specific hazard estimation based on balancing representations.
  arXiv  Detail & Related papers  (2021-10-26T20:13:17Z)
• Efficient Causal Inference from Combined Observational and Interventional Data through Causal Reductions [68.6505592770171]
  Unobserved confounding is one of the main challenges when estimating causal effects. We propose a novel causal reduction method that replaces an arbitrary number of possibly high-dimensional latent confounders. We propose a learning algorithm to estimate the parameterized reduced model jointly from observational and interventional data.
  arXiv  Detail & Related papers  (2021-03-08T14:29:07Z)
• Increasing the efficiency of randomized trial estimates via linear adjustment for a prognostic score [59.75318183140857]
  Estimating causal effects from randomized experiments is central to clinical research. Most methods for historical borrowing achieve reductions in variance by sacrificing strict type-I error rate control.
  arXiv  Detail & Related papers  (2020-12-17T21:10:10Z)
• 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)
• Performance metrics for intervention-triggering prediction models do not reflect an expected reduction in outcomes from using the model [71.9860741092209]
  Clinical researchers often select among and evaluate risk prediction models. Standard metrics calculated from retrospective data are only related to model utility under certain assumptions. When predictions are delivered repeatedly throughout time, the relationship between standard metrics and utility is further complicated.
  arXiv  Detail & Related papers  (2020-06-02T16:26:49Z)
• Nonparametric inverse probability weighted estimators based on the highly adaptive lasso [0.966840768820136]
  Inparametric inverse probability weighted estimators are known to be inefficient and suffer from the curse of dimensionality. We propose a class of nonparametric inverse probability weighted estimators in which the weighting mechanism is estimated via undersmoothing of the highly adaptive lasso. Our developments have broad implications for the construction of efficient inverse probability weighted estimators in large statistical models and a variety of problem settings.
  arXiv  Detail & Related papers  (2020-05-22T17:49:46Z)
• Machine learning for causal inference: on the use of cross-fit estimators [77.34726150561087]
  Doubly-robust cross-fit estimators have been proposed to yield better statistical properties. We conducted a simulation study to assess the performance of several estimators for the average causal effect (ACE) When used with machine learning, the doubly-robust cross-fit estimators substantially outperformed all of the other estimators in terms of bias, variance, and confidence interval coverage.
  arXiv  Detail & Related papers  (2020-04-21T23:09:55Z)

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.