HNCI: High-Dimensional Network Causal Inference

• URL: http://arxiv.org/abs/2412.18568v1
• Date: Tue, 24 Dec 2024 17:41:41 GMT
• Title: HNCI: High-Dimensional Network Causal Inference
• Authors: Wenqin Du, Rundong Ding, Yingying Fan, Jinchi Lv,
• Abstract summary: We suggest a new method of high-dimensional network causal inference (HNCI) that provides both valid confidence interval on the average direct treatment effect on the treated (ADET) and valid confidence set for the neighborhood size for interference effect.
• Score: 4.024850952459758
• License:
• Abstract: The problem of evaluating the effectiveness of a treatment or policy commonly appears in causal inference applications under network interference. In this paper, we suggest the new method of high-dimensional network causal inference (HNCI) that provides both valid confidence interval on the average direct treatment effect on the treated (ADET) and valid confidence set for the neighborhood size for interference effect. We exploit the model setting in Belloni et al. (2022) and allow certain type of heterogeneity in node interference neighborhood sizes. We propose a linear regression formulation of potential outcomes, where the regression coefficients correspond to the underlying true interference function values of nodes and exhibit a latent homogeneous structure. Such a formulation allows us to leverage existing literature from linear regression and homogeneity pursuit to conduct valid statistical inferences with theoretical guarantees. The resulting confidence intervals for the ADET are formally justified through asymptotic normalities with estimable variances. We further provide the confidence set for the neighborhood size with theoretical guarantees exploiting the repro samples approach. The practical utilities of the newly suggested methods are demonstrated through simulation and real data examples.

Related papers

• Confidence Interval Construction and Conditional Variance Estimation with Dense ReLU Networks [11.218066045459778]
  This paper addresses the problems of conditional variance estimation and confidence interval construction in nonparametric regression using dense networks with the Rectified Linear Unit (ReLU) activation function. We present a residual-based framework for conditional variance estimation, deriving nonasymptotic bounds for variance estimation under both heteroscedastic and homoscedastic settings. We develop a ReLU network based robust bootstrap procedure for constructing confidence intervals for the true mean that comes with a theoretical guarantee on the coverage, providing a significant advancement in uncertainty quantification and the construction of reliable confidence intervals in deep learning settings.
  arXiv  Detail & Related papers  (2024-12-29T05:17:58Z)
• On the Role of Surrogates in Conformal Inference of Individual Causal Effects [0.0]
  We introduce underlineSurrogate-assisted underlineConformal underlineInference for underlineEfficient IunderlineNdividual underlineCausal underlineEffects (SCIENCE) SCIENCE is a framework designed to construct more efficient prediction intervals for individual treatment effects (ITEs) It is applied to the phase 3 Moderna COVE COVID-19 vaccine trial.
  arXiv  Detail & Related papers  (2024-12-16T21:36:11Z)
• Noise-Aware Differentially Private Variational Inference [5.4619385369457225]
  Differential privacy (DP) provides robust privacy guarantees for statistical inference, but this can lead to unreliable results and biases in downstream applications. We propose a novel method for noise-aware approximate Bayesian inference based on gradient variational inference. We also propose a more accurate evaluation method for noise-aware posteriors.
  arXiv  Detail & Related papers  (2024-10-25T08:18:49Z)
• Statistical Inference for Temporal Difference Learning with Linear Function Approximation [62.69448336714418]
  We study the consistency properties of TD learning with Polyak-Ruppert averaging and linear function approximation. First, we derive a novel high-dimensional probability convergence guarantee that depends explicitly on the variance and holds under weak conditions. We further establish refined high-dimensional Berry-Esseen bounds over the class of convex sets that guarantee faster rates than those in the literature.
  arXiv  Detail & Related papers  (2024-10-21T15:34:44Z)
• Conformal Counterfactual Inference under Hidden Confounding [19.190396053530417]
  Predicting potential outcomes along with its uncertainty in a counterfactual world poses the foundamental challenge in causal inference. Existing methods that construct confidence intervals for counterfactuals either rely on the assumption of strong ignorability. We propose a novel approach based on transductive weighted conformal prediction, which provides confidence intervals for counterfactual outcomes with marginal converage guarantees.
  arXiv  Detail & Related papers  (2024-05-20T21:43:43Z)
• Advancing Counterfactual Inference through Nonlinear Quantile Regression [77.28323341329461]
  We propose a framework for efficient and effective counterfactual inference implemented with neural networks. The proposed approach enhances the capacity to generalize estimated counterfactual outcomes to unseen data. Empirical results conducted on multiple datasets offer compelling support for our theoretical assertions.
  arXiv  Detail & Related papers  (2023-06-09T08:30:51Z)
• Data-Driven Influence Functions for Optimization-Based Causal Inference [105.5385525290466]
  We study a constructive algorithm that approximates Gateaux derivatives for statistical functionals by finite differencing. We study the case where probability distributions are not known a priori but need to be estimated from data.
  arXiv  Detail & Related papers  (2022-08-29T16:16:22Z)
• Dimension-Free Average Treatment Effect Inference with Deep Neural Networks [6.704751710867747]
  This paper investigates the estimation and inference of the average treatment effect (ATE) using deep neural networks (DNNs) in the potential outcomes framework. We show that both DNN estimates of ATE are consistent with dimension-free consistency rates under some assumptions on the underlying true mean regression model.
  arXiv  Detail & Related papers  (2021-12-02T19:28:37Z)
• 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)
• CoinDICE: Off-Policy Confidence Interval Estimation [107.86876722777535]
  We study high-confidence behavior-agnostic off-policy evaluation in reinforcement learning. We show in a variety of benchmarks that the confidence interval estimates are tighter and more accurate than existing methods.
  arXiv  Detail & Related papers  (2020-10-22T12:39:11Z)
• GenDICE: Generalized Offline Estimation of Stationary Values [108.17309783125398]
  We show that effective estimation can still be achieved in important applications. Our approach is based on estimating a ratio that corrects for the discrepancy between the stationary and empirical distributions. The resulting algorithm, GenDICE, is straightforward and effective.
  arXiv  Detail & Related papers  (2020-02-21T00:27:52Z)

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.