Partial Identification with Noisy Covariates: A Robust Optimization Approach

• URL: http://arxiv.org/abs/2202.10665v1
• Date: Tue, 22 Feb 2022 04:24:26 GMT
• Title: Partial Identification with Noisy Covariates: A Robust Optimization Approach
• Authors: Wenshuo Guo, Mingzhang Yin, Yixin Wang, Michael I. Jordan
• Abstract summary: Causal inference from observational datasets often relies on measuring and adjusting for covariates. We show that this robust optimization approach can extend a wide range of causal adjustment methods to perform partial identification. Across synthetic and real datasets, we find that this approach provides ATE bounds with a higher coverage probability than existing methods.
• Score: 94.10051154390237
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Causal inference from observational datasets often relies on measuring and adjusting for covariates. In practice, measurements of the covariates can often be noisy and/or biased, or only measurements of their proxies may be available. Directly adjusting for these imperfect measurements of the covariates can lead to biased causal estimates. Moreover, without additional assumptions, the causal effects are not point-identifiable due to the noise in these measurements. To this end, we study the partial identification of causal effects given noisy covariates, under a user-specified assumption on the noise level. The key observation is that we can formulate the identification of the average treatment effects (ATE) as a robust optimization problem. This formulation leads to an efficient robust optimization algorithm that bounds the ATE with noisy covariates. We show that this robust optimization approach can extend a wide range of causal adjustment methods to perform partial identification, including backdoor adjustment, inverse propensity score weighting, double machine learning, and front door adjustment. Across synthetic and real datasets, we find that this approach provides ATE bounds with a higher coverage probability than existing methods.

Related papers

• Robust Gaussian Processes via Relevance Pursuit [17.39376866275623]
  We propose and study a GP model that achieves robustness against sparse outliers by inferring data-point-specific noise levels. We show, surprisingly, that the model can be parameterized such that the associated log marginal likelihood is strongly concave in the data-point-specific noise variances.
  arXiv  Detail & Related papers  (2024-10-31T17:59:56Z)
• 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)
• Robust Estimation of Causal Heteroscedastic Noise Models [7.568978862189266]
  Student's $t$-distribution is known for its robustness in accounting for sampling variability with smaller sample sizes and extreme values without significantly altering the overall distribution shape. Our empirical evaluations demonstrate that our estimators are more robust and achieve better overall performance across synthetic and real benchmarks.
  arXiv  Detail & Related papers  (2023-12-15T02:26:35Z)
• 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)
• Label Noise: Correcting the Forward-Correction [0.0]
  Training neural network classifiers on datasets with label noise poses a risk of overfitting them to the noisy labels. We propose an approach to tackling overfitting caused by label noise. Motivated by this observation, we propose imposing a lower bound on the training loss to mitigate overfitting.
  arXiv  Detail & Related papers  (2023-07-24T19:41:19Z)
• dugMatting: Decomposed-Uncertainty-Guided Matting [83.71273621169404]
  We propose a decomposed-uncertainty-guided matting algorithm, which explores the explicitly decomposed uncertainties to efficiently and effectively improve the results. The proposed matting framework relieves the requirement for users to determine the interaction areas by using simple and efficient labeling.
  arXiv  Detail & Related papers  (2023-06-02T11:19:50Z)
• 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)
• Adaptive Noisy Data Augmentation for Regularized Estimation and Inference in Generalized Linear Models [15.817569026827451]
  We propose the AdaPtive Noise Augmentation (PANDA) procedure to regularize the estimation and inference of generalized linear models (GLMs) We demonstrate the superior or similar performance of PANDA against the existing approaches of the same type of regularizers in simulated and real-life data.
  arXiv  Detail & Related papers  (2022-04-18T22:02:37Z)
• Differentiable Annealed Importance Sampling and the Perils of Gradient Noise [68.44523807580438]
  Annealed importance sampling (AIS) and related algorithms are highly effective tools for marginal likelihood estimation. Differentiability is a desirable property as it would admit the possibility of optimizing marginal likelihood as an objective. We propose a differentiable algorithm by abandoning Metropolis-Hastings steps, which further unlocks mini-batch computation.
  arXiv  Detail & Related papers  (2021-07-21T17:10:14Z)
• 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)

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.