A Two-Stage Interpretable Matching Framework for Causal Inference

• URL: http://arxiv.org/abs/2504.09635v1
• Date: Sun, 13 Apr 2025 16:17:52 GMT
• Title: A Two-Stage Interpretable Matching Framework for Causal Inference
• Authors: Sahil Shikalgar, Md. Noor-E-Alam,
• Abstract summary: Matching in causal inference from observational data aims to construct treatment and control groups with similar distributions of covariables.<n>We introduce a novel Two-stage Interpretable Matching framework for transparent and interpretable covariable matching.<n>We use these high- quality matches to estimate the conditional average treatment effects (CATEs)<n>Our results demonstrate that TIM improves CATE estimates, increases multivariate overlap, and scales effectively to high-dimensional data.
• Score: 0.6215404942415159
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Matching in causal inference from observational data aims to construct treatment and control groups with similar distributions of covariates, thereby reducing confounding and ensuring an unbiased estimation of treatment effects. This matched sample closely mimics a randomized controlled trial (RCT), thus improving the quality of causal estimates. We introduce a novel Two-stage Interpretable Matching (TIM) framework for transparent and interpretable covariate matching. In the first stage, we perform exact matching across all available covariates. For treatment and control units without an exact match in the first stage, we proceed to the second stage. Here, we iteratively refine the matching process by removing the least significant confounder in each iteration and attempting exact matching on the remaining covariates. We learn a distance metric for the dropped covariates to quantify closeness to the treatment unit(s) within the corresponding strata. We used these high- quality matches to estimate the conditional average treatment effects (CATEs). To validate TIM, we conducted experiments on synthetic datasets with varying association structures and correlations. We assessed its performance by measuring bias in CATE estimation and evaluating multivariate overlap between treatment and control groups before and after matching. Additionally, we apply TIM to a real-world healthcare dataset from the Centers for Disease Control and Prevention (CDC) to estimate the causal effect of high cholesterol on diabetes. Our results demonstrate that TIM improves CATE estimates, increases multivariate overlap, and scales effectively to high-dimensional data, making it a robust tool for causal inference in observational data.

Related papers

• Representation Learning Preserving Ignorability and Covariate Matching for Treatment Effects [18.60804431844023]
  Estimating treatment effects from observational data is challenging due to hidden confounding. A common framework to address both hidden confounding and selection bias is missing.
  arXiv  Detail & Related papers  (2025-04-29T09:33:56Z)
• A Partial Initialization Strategy to Mitigate the Overfitting Problem in CATE Estimation with Hidden Confounding [44.874826691991565]
  Estimating the conditional average treatment effect (CATE) from observational data plays a crucial role in areas such as e-commerce, healthcare, and economics.<n>Existing studies mainly rely on the strong ignorability assumption that there are no hidden confounders.<n>Data collected from randomized controlled trials (RCT) do not suffer from confounding but are usually limited by a small sample size.
  arXiv  Detail & Related papers  (2025-01-15T15:58:16Z)
• Difference-in-Differences with Time-varying Continuous Treatments using Double/Debiased Machine Learning [0.0]
  We propose a difference-in-differences (DiD) method for continuous treatment and multiple time periods. Our framework assesses the average treatment effect on the treated (ATET) when comparing two non-zero treatment doses.
  arXiv  Detail & Related papers  (2024-10-28T15:10:43Z)
• 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)
• Counterfactual Data Augmentation with Contrastive Learning [27.28511396131235]
  We introduce a model-agnostic data augmentation method that imputes the counterfactual outcomes for a selected subset of individuals. We use contrastive learning to learn a representation space and a similarity measure such that in the learned representation space close individuals identified by the learned similarity measure have similar potential outcomes. This property ensures reliable imputation of counterfactual outcomes for the individuals with close neighbors from the alternative treatment group.
  arXiv  Detail & Related papers  (2023-11-07T00:36:51Z)
• 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)
• Hierarchical Semi-Supervised Contrastive Learning for Contamination-Resistant Anomaly Detection [81.07346419422605]
  Anomaly detection aims at identifying deviant samples from the normal data distribution. Contrastive learning has provided a successful way to sample representation that enables effective discrimination on anomalies. We propose a novel hierarchical semi-supervised contrastive learning framework, for contamination-resistant anomaly detection.
  arXiv  Detail & Related papers  (2022-07-24T18:49:26Z)
• 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)
• Estimating Conditional Average Treatment Effects with Missing Treatment Information [20.83151214072516]
  Estimating conditional average treatment effects (CATE) is challenging when treatment information is missing. In this paper, we analyze CATE estimation in the setting with missing treatments. We develop MTRNet, a novel CATE estimation algorithm.
  arXiv  Detail & Related papers  (2022-03-02T21:23:25Z)
• Treatment Effect Risk: Bounds and Inference [58.442274475425144]
  Since the average treatment effect measures the change in social welfare, even if positive, there is a risk of negative effect on, say, some 10% of the population. In this paper we consider how to nonetheless assess this important risk measure, formalized as the conditional value at risk (CVaR) of the ITE distribution. Some bounds can also be interpreted as summarizing a complex CATE function into a single metric and are of interest independently of being a bound.
  arXiv  Detail & Related papers  (2022-01-15T17:21:26Z)
• Assessment of Treatment Effect Estimators for Heavy-Tailed Data [70.72363097550483]
  A central obstacle in the objective assessment of treatment effect (TE) estimators in randomized control trials (RCTs) is the lack of ground truth (or validation set) to test their performance. We provide a novel cross-validation-like methodology to address this challenge. We evaluate our methodology across 709 RCTs implemented in the Amazon supply chain.
  arXiv  Detail & Related papers  (2021-12-14T17:53:01Z)
• 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)

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.