A Causal Inference Framework for Data Rich Environments

• URL: http://arxiv.org/abs/2504.01702v1
• Date: Wed, 02 Apr 2025 13:04:26 GMT
• Title: A Causal Inference Framework for Data Rich Environments
• Authors: Alberto Abadie, Anish Agarwal, Devavrat Shah,
• Abstract summary: We show how classic models for potential outcomes and treatment assignments fit within our framework.<n>For any estimator that has a fast enough estimation error rate for a certain nuisance parameter, we establish it is consistent for these various causal parameters.
• Score: 17.588417435132538
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a formal model for counterfactual estimation with unobserved confounding in "data-rich" settings, i.e., where there are a large number of units and a large number of measurements per unit. Our model provides a bridge between the structural causal model view of causal inference common in the graphical models literature with that of the latent factor model view common in the potential outcomes literature. We show how classic models for potential outcomes and treatment assignments fit within our framework. We provide an identification argument for the average treatment effect, the average treatment effect on the treated, and the average treatment effect on the untreated. For any estimator that has a fast enough estimation error rate for a certain nuisance parameter, we establish it is consistent for these various causal parameters. We then show principal component regression is one such estimator that leads to consistent estimation, and we analyze the minimal smoothness required of the potential outcomes function for consistency.

Related papers

• Model-free Methods for Event History Analysis and Efficient Adjustment (PhD Thesis) [55.2480439325792]
  This thesis is a series of independent contributions to statistics unified by a model-free perspective.<n>The first chapter elaborates on how a model-free perspective can be used to formulate flexible methods that leverage prediction techniques from machine learning.<n>The second chapter studies the concept of local independence, which describes whether the evolution of one process is directly influenced by another.
  arXiv  Detail & Related papers  (2025-02-11T19:24:09Z)
• Deep Learning Methods for the Noniterative Conditional Expectation G-Formula for Causal Inference from Complex Observational Data [3.0958655016140892]
  The g-formula can be used to estimate causal effects of sustained treatment strategies using observational data. Parametric models are subject to model misspecification, which may result in biased causal estimates. We propose a unified deep learning framework for the NICE g-formula estimator.
  arXiv  Detail & Related papers  (2024-10-28T21:00:46Z)
• Influence Functions for Scalable Data Attribution in Diffusion Models [52.92223039302037]
  Diffusion models have led to significant advancements in generative modelling. Yet their widespread adoption poses challenges regarding data attribution and interpretability. We develop an influence functions framework to address these challenges.
  arXiv  Detail & Related papers  (2024-10-17T17:59:02Z)
• C-XGBoost: A tree boosting model for causal effect estimation [8.246161706153805]
  Causal effect estimation aims at estimating the Average Treatment Effect as well as the Conditional Average Treatment Effect of a treatment to an outcome from the available data. We propose a new causal inference model, named C-XGBoost, for the prediction of potential outcomes.
  arXiv  Detail & Related papers  (2024-03-31T17:43:37Z)
• Linked shrinkage to improve estimation of interaction effects in regression models [0.0]
  We develop an estimator that adapts well to two-way interaction terms in a regression model. We evaluate the potential of the model for inference, which is notoriously hard for selection strategies. Our models can be very competitive to a more advanced machine learner, like random forest, even for fairly large sample sizes.
  arXiv  Detail & Related papers  (2023-09-25T10:03:39Z)
• 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)
• Variable Importance Matching for Causal Inference [73.25504313552516]
  We describe a general framework called Model-to-Match that achieves these goals. Model-to-Match uses variable importance measurements to construct a distance metric. We operationalize the Model-to-Match framework with LASSO.
  arXiv  Detail & Related papers  (2023-02-23T00:43:03Z)
• Empirical Analysis of Model Selection for Heterogeneous Causal Effect Estimation [24.65301562548798]
  We study the problem of model selection in causal inference, specifically for conditional average treatment effect (CATE) estimation. We conduct an empirical analysis to benchmark the surrogate model selection metrics introduced in the literature, as well as the novel ones introduced in this work.
  arXiv  Detail & Related papers  (2022-11-03T16:26:06Z)
• An evaluation framework for comparing causal inference models [3.1372269816123994]
  We use the proposed evaluation methodology to compare several state-of-the-art causal effect estimation models. The main motivation behind this approach is the elimination of the influence of a small number of instances or simulation on the benchmarking process.
  arXiv  Detail & Related papers  (2022-08-31T21:04:20Z)
• Estimation of Bivariate Structural Causal Models by Variational Gaussian Process Regression Under Likelihoods Parametrised by Normalising Flows [74.85071867225533]
  Causal mechanisms can be described by structural causal models. One major drawback of state-of-the-art artificial intelligence is its lack of explainability.
  arXiv  Detail & Related papers  (2021-09-06T14:52:58Z)
• Efficient Causal Inference from Combined Observational and Interventional Data through Causal Reductions [68.6505592770171]
  Unobserved confounding is one of the main challenges when estimating causal effects. We propose a novel causal reduction method that replaces an arbitrary number of possibly high-dimensional latent confounders. We propose a learning algorithm to estimate the parameterized reduced model jointly from observational and interventional data.
  arXiv  Detail & Related papers  (2021-03-08T14:29:07Z)
• Sparse Bayesian Causal Forests for Heterogeneous Treatment Effects Estimation [0.0]
  This paper develops a sparsity-inducing version of Bayesian Causal Forests. It is designed to estimate heterogeneous treatment effects using observational data.
  arXiv  Detail & Related papers  (2021-02-12T15:24:50Z)

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.