Neighborhood Adaptive Estimators for Causal Inference under Network Interference

• URL: http://arxiv.org/abs/2212.03683v1
• Date: Wed, 7 Dec 2022 14:53:47 GMT
• Title: Neighborhood Adaptive Estimators for Causal Inference under Network Interference
• Authors: Alexandre Belloni, Fei Fang and Alexander Volfovsky
• Abstract summary: We consider the violation of the classical no-interference assumption, meaning that the treatment of one individuals might affect the outcomes of another. To make interference tractable, we consider a known network that describes how interference may travel. We study estimators for the average direct treatment effect on the treated in such a setting.
• Score: 152.4519491244279
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Estimating causal effects has become an integral part of most applied fields. Solving these modern causal questions requires tackling violations of many classical causal assumptions. In this work we consider the violation of the classical no-interference assumption, meaning that the treatment of one individuals might affect the outcomes of another. To make interference tractable, we consider a known network that describes how interference may travel. However, unlike previous work in this area, the radius (and intensity) of the interference experienced by a unit is unknown and can depend on different sub-networks of those treated and untreated that are connected to this unit. We study estimators for the average direct treatment effect on the treated in such a setting. The proposed estimator builds upon a Lepski-like procedure that searches over the possible relevant radii and treatment assignment patterns. In contrast to previous work, the proposed procedure aims to approximate the relevant network interference patterns. We establish oracle inequalities and corresponding adaptive rates for the estimation of the interference function. We leverage such estimates to propose and analyze two estimators for the average direct treatment effect on the treated. We address several challenges steaming from the data-driven creation of the patterns (i.e. feature engineering) and the network dependence. In addition to rates of convergence, under mild regularity conditions, we show that one of the proposed estimators is asymptotically normal and unbiased.

Related papers

• Higher-Order Causal Message Passing for Experimentation with Complex Interference [6.092214762701847]
  We introduce a new class of estimators based on causal message-passing, specifically designed for settings with pervasive, unknown interference. Our estimator draws on information from the sample mean and variance of unit outcomes and treatments over time, enabling efficient use of observed data.
  arXiv  Detail & Related papers  (2024-11-01T18:00:51Z)
• Targeted Machine Learning for Average Causal Effect Estimation Using the Front-Door Functional [3.0232957374216953]
  evaluating the average causal effect (ACE) of a treatment on an outcome often involves overcoming the challenges posed by confounding factors in observational studies. Here, we introduce novel estimation strategies for the front-door criterion based on the targeted minimum loss-based estimation theory. We demonstrate the applicability of these estimators to analyze the effect of early stage academic performance on future yearly income.
  arXiv  Detail & Related papers  (2023-12-15T22:04:53Z)
• Causal Message Passing for Experiments with Unknown and General Network Interference [5.294604210205507]
  We introduce a new framework to accommodate complex and unknown network interference. Our framework, termed causal message-passing, is grounded in high-dimensional approximate message passing methodology. We demonstrate the effectiveness of this approach across five numerical scenarios.
  arXiv  Detail & Related papers  (2023-11-14T17:31:50Z)
• Doubly Robust Proximal Causal Learning for Continuous Treatments [56.05592840537398]
  We propose a kernel-based doubly robust causal learning estimator for continuous treatments. We show that its oracle form is a consistent approximation of the influence function. We then provide a comprehensive convergence analysis in terms of the mean square error.
  arXiv  Detail & Related papers  (2023-09-22T12:18:53Z)
• Bayesian Counterfactual Mean Embeddings and Off-Policy Evaluation [10.75801980090826]
  We present three novel Bayesian methods to estimate the expectation of the ultimate treatment effect. These methods differ on the source of uncertainty considered and allow for combining two sources of data. We generalize these ideas to the off-policy evaluation framework.
  arXiv  Detail & Related papers  (2022-11-02T23:39:36Z)
• 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)
• Causal Inference Under Unmeasured Confounding With Negative Controls: A Minimax Learning Approach [84.29777236590674]
  We study the estimation of causal parameters when not all confounders are observed and instead negative controls are available. Recent work has shown how these can enable identification and efficient estimation via two so-called bridge functions.
  arXiv  Detail & Related papers  (2021-03-25T17:59:19Z)
• Valid Causal Inference with (Some) Invalid Instruments [24.794879633855373]
  We show how to perform consistent IV estimation despite violations of the exclusion assumption. We achieve accurate estimates of conditional average treatment effects using an ensemble of deep network-based estimators.
  arXiv  Detail & Related papers  (2020-06-19T21:09:26Z)
• Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference [73.23326654892963]
  We propose a matching method that recovers direct treatment effects from randomized experiments where units are connected in an observed network. Our method matches units almost exactly on counts of unique subgraphs within their neighborhood graphs.
  arXiv  Detail & Related papers  (2020-03-02T15:21:20Z)
• Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effects [61.03579766573421]
  We study estimation of individual-level causal effects, such as a single patient's response to alternative medication. We devise representation learning algorithms that minimize our bound, by regularizing the representation's induced treatment group distance. We extend these algorithms to simultaneously learn a weighted representation to further reduce treatment group distances.
  arXiv  Detail & Related papers  (2020-01-21T10:16:33Z)

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.