Direct Bias-Correction Term Estimation for Propensity Scores and Average Treatment Effect Estimation

• URL: http://arxiv.org/abs/2509.22122v1
• Date: Fri, 26 Sep 2025 09:45:12 GMT
• Title: Direct Bias-Correction Term Estimation for Propensity Scores and Average Treatment Effect Estimation
• Abstract summary: This study considers the estimation of the average treatment effect (ATE)<n>We estimate the propensity score through direct bias-correction term estimation.<n>We present a general framework for this direct bias-correction term estimation approach.
• Score: 6.44705221140412
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This study considers the estimation of the average treatment effect (ATE). For ATE estimation, we estimate the propensity score through direct bias-correction term estimation. Let $\{(X_i, D_i, Y_i)\}_{i=1}^{n}$ be the observations, where $X_i \in \mathbb{R}^p$ denotes $p$-dimensional covariates, $D_i \in \{0, 1\}$ denotes a binary treatment assignment indicator, and $Y_i \in \mathbb{R}$ is an outcome. In ATE estimation, the bias-correction term $h_0(X_i, D_i) = \frac{1[D_i = 1]}{e_0(X_i)} - \frac{1[D_i = 0]}{1 - e_0(X_i)}$ plays an important role, where $e_0(X_i)$ is the propensity score, the probability of being assigned treatment $1$. In this study, we propose estimating $h_0$ (or equivalently the propensity score $e_0$) by directly minimizing the prediction error of $h_0$. Since the bias-correction term $h_0$ is essential for ATE estimation, this direct approach is expected to improve estimation accuracy for the ATE. For example, existing studies often employ maximum likelihood or covariate balancing to estimate $e_0$, but these approaches may not be optimal for accurately estimating $h_0$ or the ATE. We present a general framework for this direct bias-correction term estimation approach from the perspective of Bregman divergence minimization and conduct simulation studies to evaluate the effectiveness of the proposed method.

Related papers

• CollabEval: Statistically Efficient Collaborative Model Evaluation via Matrix Completion [65.53153291902454]
  Collaborative Evaluation (CollabEval) is a simple, effective, and principled method for exploiting dependencies between historical runs of different models on the same tasks to improve statistical efficiency.<n>We build a low-rank approximation of the score matrix, and use the reconstructed values to guarantee unbiased estimates of the true evaluation metric mean.<n>We find that CollabEval substantially reduces the mean confidence interval size, and the mean squared error of the point estimate, compared to baseline methods at the same annotation budget.
  arXiv  Detail & Related papers  (2026-07-06T13:22:04Z)
• Optimally taming biases in black-box models for efficient semiparametric estimation [13.159615239651258]
  We show that the standard DML rate is not optimal in the regime where auxiliary function $mathbbE[T|X=x]$ cannot be consistently estimated.<n>Our results reveal a new principle: the first-order error of nuisance estimation can be eliminated without imposing any additional assumptions.
  arXiv  Detail & Related papers  (2026-06-04T16:36:18Z)
• Bandit Convex Optimization with Gradient Prediction Adaptivity [56.816177049016794]
  We investigate whether optimistic gradient predictions can improve worst-case regret guarantees in a prediction-adaptive manner.<n>The key idea is a novel variance-reduced gradient estimator whose variance scales with the prediction error rather than the gradient norm.<n>We establish an information-theoretic lower bound that scales as $(sqrtmathbbE[S_T])$, providing a fundamental characterization of the best achievable prediction-adaptive regret.
  arXiv  Detail & Related papers  (2026-05-21T08:57:38Z)
• Convergence Rate of a Functional Learning Method for Contextual Stochastic Optimization [0.6015898117103067]
  We analyze a simultaneous learning-and-optimization algorithm that jointly estimates the conditional expectation and optimize the outer objective.<n>We establish that the method achieves a convergence rate of order $mathcalObig (1/sqrtNbig)$, where $N$ denotes the number of observed pairs.
  arXiv  Detail & Related papers  (2026-03-13T14:53:35Z)
• Convex Regression with a Penalty [0.0]
  A common way to estimate an unknown convex regression function $f_0: Omega subset mathbbRd rightarrow mathbbR$ from a set of $n$ noisy observations is to fit a convex function that minimizes the sum of squared errors.<n>We introduce a new estimator of $f_0$ that avoids this overfitting by minimizing a penalty on the subgradient while enforcing an upper bound $s_n$ on the sum of squared errors.
  arXiv  Detail & Related papers  (2025-09-24T06:19:21Z)
• Log-Sum-Exponential Estimator for Off-Policy Evaluation and Learning [50.93804891554481]
  We introduce a novel estimator based on the log-sum-exponential (LSE) operator, which outperforms traditional inverse propensity score estimators.<n>Our LSE estimator demonstrates variance reduction and robustness under heavy-tailed conditions.<n>In the off-policy learning scenario, we establish bounds on the regret -- the performance gap between our LSE estimator and the optimal policy.
  arXiv  Detail & Related papers  (2025-06-07T17:37:10Z)
• Regression-Based Estimation of Causal Effects in the Presence of Selection Bias and Confounding [52.1068936424622]
  We consider the problem of estimating the expected causal effect $E[Y|do(X)]$ for a target variable $Y$ when treatment $X$ is set by intervention.<n>In settings without selection bias or confounding, $E[Y|do(X)] = E[Y|X]$, which can be estimated using standard regression methods.<n>We propose a framework that incorporates both selection bias and confounding.
  arXiv  Detail & Related papers  (2025-03-26T13:43:37Z)
• Computational-Statistical Tradeoffs at the Next-Token Prediction Barrier: Autoregressive and Imitation Learning under Misspecification [50.717692060500696]
  Next-token prediction with the logarithmic loss is a cornerstone of autoregressive sequence modeling.<n>Next-token prediction can be made robust so as to achieve $C=tilde O(H)$, representing moderate error amplification.<n>No computationally efficient algorithm can achieve sub-polynomial approximation factor $C=e(log H)1-Omega(1)$.
  arXiv  Detail & Related papers  (2025-02-18T02:52:00Z)
• Beyond likelihood ratio bias: Nested multi-time-scale stochastic approximation for likelihood-free parameter estimation [49.78792404811239]
  We study inference in simulation-based models where the analytical form of the likelihood is unknown.<n>We use a ratio-free nested multi-time-scale approximation (SA) method that simultaneously tracks the score and drives the parameter update.<n>We show that our algorithm can eliminate the original bias $Obig(sqrtfrac1Nbig)$ and accelerate the convergence rate from $Obig(beta_k+sqrtfracalpha_kNbig)$.
  arXiv  Detail & Related papers  (2024-11-20T02:46:15Z)
• Counterfactual Uncertainty Quantification of Factual Estimand of Efficacy from Before-and-After Treatment Repeated Measures Randomized Controlled Trials [1.3461364647443341]
  This article quantifies the uncertainty reduction achievable for textitcounterfactual estimand, and cautions against potential bias when the estimand uses Digital Twins.
  arXiv  Detail & Related papers  (2024-11-14T18:01:02Z)
• Unbiased least squares regression via averaged stochastic gradient descent [0.0]
  We consider an on-line least squares regression problem with optimal solution $theta*$ and Hessian matrix H. For $kge2$, we provide an unbiased estimator of $theta*$ that is a modification of the time-average estimator.
  arXiv  Detail & Related papers  (2024-06-26T11:39:22Z)
• Mind the Gap: A Causal Perspective on Bias Amplification in Prediction & Decision-Making [58.06306331390586]
  We introduce the notion of a margin complement, which measures how much a prediction score $S$ changes due to a thresholding operation. We show that under suitable causal assumptions, the influences of $X$ on the prediction score $S$ are equal to the influences of $X$ on the true outcome $Y$.
  arXiv  Detail & Related papers  (2024-05-24T11:22:19Z)
• The out-of-sample $R^2$: estimation and inference [0.0]
  We define the out-of-sample $R2$ as a comparison of two predictive models. We exploit recent theoretical advances on uncertainty of data splitting estimates to provide a standard error for the $hatR2$.
  arXiv  Detail & Related papers  (2023-02-10T09:29:57Z)
• SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression [68.66245730450915]
  We develop an improved method for debiasing predictions and estimating frequentist uncertainty for practical datasets. Our main contribution is SLOE, an estimator of the signal strength with convergence guarantees that reduces the computation time of estimation and inference by orders of magnitude.
  arXiv  Detail & Related papers  (2021-03-23T17:48:56Z)
• Debiasing Distributed Second Order Optimization with Surrogate Sketching and Scaled Regularization [101.5159744660701]
  In distributed second order optimization, a standard strategy is to average many local estimates, each of which is based on a small sketch or batch of the data. Here, we introduce a new technique for debiasing the local estimates, which leads to both theoretical and empirical improvements in the convergence rate of distributed second order methods.
  arXiv  Detail & Related papers  (2020-07-02T18:08:14Z)

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.