Shrinkage-Based Regressions with Many Related Treatments

• URL: http://arxiv.org/abs/2507.01202v1
• Date: Tue, 01 Jul 2025 21:44:30 GMT
• Title: Shrinkage-Based Regressions with Many Related Treatments
• Authors: Enes Dilber, Colin Gray,
• Abstract summary: Practitioners often want to disentangle the effects of many related, partially-overlapping treatments.<n>Common approaches that estimate separate treatment coefficients are too noisy for practical decision-making.<n>We propose a computationally light model that uses a customized ridge regression to move between a heterogeneous and a homogenous model.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: When using observational causal models, practitioners often want to disentangle the effects of many related, partially-overlapping treatments. Examples include estimating treatment effects of different marketing touchpoints, ordering different types of products, or signing up for different services. Common approaches that estimate separate treatment coefficients are too noisy for practical decision-making. We propose a computationally light model that uses a customized ridge regression to move between a heterogeneous and a homogenous model: it substantially reduces MSE for the effects of each individual sub-treatment while allowing us to easily reconstruct the effects of an aggregated treatment. We demonstrate the properties of this estimator in theory and simulation, and illustrate how it has unlocked targeted decision-making at Wayfair.

Related papers

• A Causal Inference Framework for Data Rich Environments [17.588417435132538]
  We show how classic models for potential outcomes and treatment assignments fit within our framework.<n>For any estimator that has a fast enough estimation error rate for a certain nuisance parameter, we establish it is consistent for these various causal parameters.
  arXiv  Detail & Related papers  (2025-04-02T13:04:26Z)
• Disentangled Representation via Variational AutoEncoder for Continuous Treatment Effect Estimation [1.105274635981989]
  We propose a novel Dose-Response curve estimator via Variational AutoEncoder (DRVAE) We show that our model outperforms the current state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-06-04T13:41:07Z)
• Individualized Multi-Treatment Response Curves Estimation using RBF-net with Shared Neurons [1.1119247609126184]
  Our non-parametric modeling of the response curves relies on radial basis function (RBF)-nets with shared hidden neurons. Applying our proposed method to MIMIC data, we obtain several interesting findings related to the impact of different treatment strategies on the length of ICU stay and 12-hour SOFA score for sepsis patients who are home-discharged.
  arXiv  Detail & Related papers  (2024-01-29T21:13:01Z)
• 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)
• Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability [82.29775890542967]
  Estimating personalized effects of treatments is a complex, yet pervasive problem. Recent developments in the machine learning literature on heterogeneous treatment effect estimation gave rise to many sophisticated, but opaque, tools. We use post-hoc feature importance methods to identify features that influence the model's predictions.
  arXiv  Detail & Related papers  (2022-06-16T17:59:05Z)
• A Twin Neural Model for Uplift [59.38563723706796]
  Uplift is a particular case of conditional treatment effect modeling. We propose a new loss function defined by leveraging a connection with the Bayesian interpretation of the relative risk. We show our proposed method is competitive with the state-of-the-art in simulation setting and on real data from large scale randomized experiments.
  arXiv  Detail & Related papers  (2021-05-11T16:02:39Z)
• NCoRE: Neural Counterfactual Representation Learning for Combinations of Treatments [26.991483018857803]
  We present Neural Counterfactual Relation Estimation (NCoRE), a new method for learning counterfactual representations in the combination treatment setting. NCoRE is based on a novel branched conditional neural representation that includes learnt treatment interaction modulators to infer the potential causal generative process underlying the combination of multiple treatments.
  arXiv  Detail & Related papers  (2021-03-20T13:25:00Z)
• 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)
• 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)
• Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations [114.16762407465427]
  We introduce the Counterfactual Recurrent Network (CRN) to estimate treatment effects over time. CRN uses domain adversarial training to build balancing representations of the patient history. We show how our model achieves lower error in estimating counterfactuals and in choosing the correct treatment and timing of treatment.
  arXiv  Detail & Related papers  (2020-02-10T20:47:36Z)
• 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.