C-Learner: Constrained Learning for Causal Inference

• URL: http://arxiv.org/abs/2405.09493v5
• Date: Fri, 27 Jun 2025 08:45:28 GMT
• Title: C-Learner: Constrained Learning for Causal Inference
• Authors: Tiffany Tianhui Cai, Yuri Fonseca, Kaiwen Hou, Hongseok Namkoong,
• Abstract summary: We propose a novel debiasing approach that achieves the best weighting of both worlds, producing stable plug-in estimates.<n>Our constrained learning framework solves for the best plug-in estimator under the constraint that the first-order error with respect to the plugged-in quantity is zero.
• Score: 5.395560682099634
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Popular debiased estimation methods for causal inference -- such as augmented inverse propensity weighting and targeted maximum likelihood estimation -- enjoy desirable asymptotic properties like statistical efficiency and double robustness but they can produce unstable estimates when there is limited overlap between treatment and control, requiring additional assumptions or ad hoc adjustments in practice (e.g., truncating propensity scores). In contrast, simple plug-in estimators are stable but lack desirable asymptotic properties. We propose a novel debiasing approach that achieves the best of both worlds, producing stable plug-in estimates with desirable asymptotic properties. Our constrained learning framework solves for the best plug-in estimator under the constraint that the first-order error with respect to the plugged-in quantity is zero, and can leverage flexible model classes including neural networks and tree ensembles. In several experimental settings, including ones in which we handle text-based covariates by fine-tuning language models, our constrained learning-based estimator outperforms basic versions of one-step estimation and targeting in challenging settings with limited overlap between treatment and control, and performs similarly otherwise.

Related papers

• Doubly robust outlier resistant inference on causal treatment effect [0.0]
  Outliers can severely distort causal effect estimation in observational studies.<n>We propose a doubly robust point estimator for the average treatment effect under a contaminated model.<n>Our method consistently outperforms existing approaches in both accuracy and robustness.
  arXiv  Detail & Related papers  (2025-07-23T11:58:54Z)
• Off-policy estimation with adaptively collected data: the power of online learning [20.023469636707635]
  We consider estimation of a linear functional of the treatment effect using adaptively collected data. We propose a general reduction scheme that allows one to produce a sequence of estimates for the treatment effect via online learning.
  arXiv  Detail & Related papers  (2024-11-19T10:18:27Z)
• Semiparametric conformal prediction [79.6147286161434]
  We construct a conformal prediction set accounting for the joint correlation structure of the vector-valued non-conformity scores. We flexibly estimate the joint cumulative distribution function (CDF) of the scores. Our method yields desired coverage and competitive efficiency on a range of real-world regression problems.
  arXiv  Detail & Related papers  (2024-11-04T14:29:02Z)
• Flexible Nonparametric Inference for Causal Effects under the Front-Door Model [2.6900047294457683]
  We develop novel one-step and targeted minimum loss-based estimators for both the average treatment effect and the average treatment effect on the treated under front-door assumptions.<n>Our estimators are built on multiple parameterizations of the observed data distribution, including approaches that avoid mediator density entirely.<n>We show how these constraints can be leveraged to improve the efficiency of causal effect estimators.
  arXiv  Detail & Related papers  (2023-12-15T22:04:53Z)
• Likelihood Ratio Confidence Sets for Sequential Decision Making [51.66638486226482]
  We revisit the likelihood-based inference principle and propose to use likelihood ratios to construct valid confidence sequences. Our method is especially suitable for problems with well-specified likelihoods. We show how to provably choose the best sequence of estimators and shed light on connections to online convex optimization.
  arXiv  Detail & Related papers  (2023-11-08T00:10:21Z)
• B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding [51.74479522965712]
  We propose a meta-learner called the B-Learner, which can efficiently learn sharp bounds on the CATE function under limits on hidden confounding. We prove its estimates are valid, sharp, efficient, and have a quasi-oracle property with respect to the constituent estimators under more general conditions than existing methods.
  arXiv  Detail & Related papers  (2023-04-20T18:07:19Z)
• Post Reinforcement Learning Inference [22.117487428829488]
  We consider estimation and inference using data collected from reinforcement learning algorithms. We propose a weighted Z-estimation approach with carefully designed adaptive weights to stabilize the time-varying variance. Primary applications include dynamic treatment effect estimation and dynamic off-policy evaluation.
  arXiv  Detail & Related papers  (2023-02-17T12:53:15Z)
• Semi-Supervised Quantile Estimation: Robust and Efficient Inference in High Dimensional Settings [0.5735035463793009]
  We consider quantile estimation in a semi-supervised setting, characterized by two available data sets. We propose a family of semi-supervised estimators for the response quantile(s) based on the two data sets.
  arXiv  Detail & Related papers  (2022-01-25T10:02:23Z)
• Learning to Estimate Without Bias [57.82628598276623]
  Gauss theorem states that the weighted least squares estimator is a linear minimum variance unbiased estimation (MVUE) in linear models. In this paper, we take a first step towards extending this result to non linear settings via deep learning with bias constraints. A second motivation to BCE is in applications where multiple estimates of the same unknown are averaged for improved performance.
  arXiv  Detail & Related papers  (2021-10-24T10:23:51Z)
• Near-optimal inference in adaptive linear regression [60.08422051718195]
  Even simple methods like least squares can exhibit non-normal behavior when data is collected in an adaptive manner. We propose a family of online debiasing estimators to correct these distributional anomalies in at least squares estimation. We demonstrate the usefulness of our theory via applications to multi-armed bandit, autoregressive time series estimation, and active learning with exploration.
  arXiv  Detail & Related papers  (2021-07-05T21:05:11Z)
• Scalable Personalised Item Ranking through Parametric Density Estimation [53.44830012414444]
  Learning from implicit feedback is challenging because of the difficult nature of the one-class problem. Most conventional methods use a pairwise ranking approach and negative samplers to cope with the one-class problem. We propose a learning-to-rank approach, which achieves convergence speed comparable to the pointwise counterpart.
  arXiv  Detail & Related papers  (2021-05-11T03:38:16Z)
• Deconfounding Scores: Feature Representations for Causal Effect Estimation with Weak Overlap [140.98628848491146]
  We introduce deconfounding scores, which induce better overlap without biasing the target of estimation. We show that deconfounding scores satisfy a zero-covariance condition that is identifiable in observed data. In particular, we show that this technique could be an attractive alternative to standard regularizations.
  arXiv  Detail & Related papers  (2021-04-12T18:50:11Z)
• Instability, Computational Efficiency and Statistical Accuracy [101.32305022521024]
  We develop a framework that yields statistical accuracy based on interplay between the deterministic convergence rate of the algorithm at the population level, and its degree of (instability) when applied to an empirical object based on $n$ samples. We provide applications of our general results to several concrete classes of models, including Gaussian mixture estimation, non-linear regression models, and informative non-response models.
  arXiv  Detail & Related papers  (2020-05-22T22:30:52Z)
• Distributional robustness of K-class estimators and the PULSE [4.56877715768796]
  We prove that the classical K-class estimator satisfies such optimality by establishing a connection between K-class estimators and anchor regression. We show that it can be computed efficiently as a data-driven simulation K-class estimator. There are several settings including weak instrument settings, where it outperforms other estimators.
  arXiv  Detail & Related papers  (2020-05-07T09:39:07Z)
• 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.