Decomposition of Spillover Effects Under Misspecification:Pseudo-true Estimands and a Local--Global Extension
- URL: http://arxiv.org/abs/2602.12023v1
- Date: Thu, 12 Feb 2026 14:54:28 GMT
- Title: Decomposition of Spillover Effects Under Misspecification:Pseudo-true Estimands and a Local--Global Extension
- Authors: Yechan Park, Xiaodong Yang,
- Abstract summary: We take as primitive the marginal policy effect, defined as the effect of a small change in the treatment probability under the actual design.<n>We show that any researcher-chosen exposure mapping induces a unique pseudo-true outcome model.<n>We then focus on a setting that nests important empirical and theoretical applications in which both local network spillovers and global spillovers operate.
- Score: 4.147806823365401
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Applied work with interference typically models outcomes as functions of own treatment and a low-dimensional exposure mapping of others' treatments, even when that mapping may be misspecified. This raises a basic question: what policy object are exposure-based estimands implicitly targeting, and how should we interpret their direct and spillover components relative to the underlying policy question? We take as primitive the marginal policy effect, defined as the effect of a small change in the treatment probability under the actual experimental design, and show that any researcher-chosen exposure mapping induces a unique pseudo-true outcome model. This model is the best approximation to the underlying potential outcomes that depends only on the user-chosen exposure. Utilizing that representation, the marginal policy effect admits a canonical decomposition into exposure-based direct and spillover effects, and each component provides its optimal approximation to the corresponding oracle objects that would be available if interference were fully known. We then focus on a setting that nests important empirical and theoretical applications in which both local network spillovers and global spillovers, such as market equilibrium, operate. There, the marginal policy effect further decomposes asymptotically into direct, local, and global channels. An important implication is that many existing methods are more robust than previously understood once we reinterpret their targets as channel-specific components of this pseudo-true policy estimand. Simulations and a semi-synthetic experiment calibrated to a large cash-transfer experiment show that these components can be recovered in realistic experimental designs.
Related papers
- Causal Inference on Networks under Misspecified Exposure Mappings: A Partial Identification Framework [66.59051888063665]
We propose a novel partial identification framework for causal inference on networks.<n>We derive sharp upper and lower bounds on direct and spillover effects under misspecifications of the exposure mapping.<n>Our experiments show the bounds remain informative and provide reliable conclusions under misspecification of exposure mappings.
arXiv Detail & Related papers (2026-02-03T12:27:11Z) - Data Fusion for Partial Identification of Causal Effects [62.56890808004615]
We propose a novel partial identification framework that enables researchers to answer key questions.<n>Is the causal effect positive or negative? and How severe must assumption violations be to overturn this conclusion?<n>We apply our framework to the Project STAR study, which investigates the effect of classroom size on students' third-grade standardized test performance.
arXiv Detail & Related papers (2025-05-30T07:13:01Z) - Proximal Inference on Population Intervention Indirect Effect [8.296034406842345]
Population intervention indirect effect (PIIE) is a novel mediation effect representing the indirect component of the population intervention effect.<n>This paper proposes a novel PIIE identification framework in settings where unmeasured confounders influence exposure-outcome, exposure-mediator, and mediator-outcome relationships.
arXiv Detail & Related papers (2025-04-16T08:14:55Z) - Collaborative Value Function Estimation Under Model Mismatch: A Federated Temporal Difference Analysis [55.13545823385091]
Federated reinforcement learning (FedRL) enables collaborative learning while preserving data privacy by preventing direct data exchange between agents.<n>In real-world applications, each agent may experience slightly different transition dynamics, leading to inherent model mismatches.<n>We show that even moderate levels of information sharing significantly mitigate environment-specific errors.
arXiv Detail & Related papers (2025-03-21T18:06:28Z) - Towards Understanding Extrapolation: a Causal Lens [53.15488984371969]
We provide a theoretical understanding of when extrapolation is possible and offer principled methods to achieve it.<n>Under this formulation, we cast the extrapolation problem into a latent-variable identification problem.<n>Our theory reveals the intricate interplay between the underlying manifold's smoothness and the shift properties.
arXiv Detail & Related papers (2025-01-15T21:29:29Z) - Targeted Sequential Indirect Experiment Design [4.262342157729123]
hypotheses concern specific aspects of complex, imperfectly understood or entirely unknown mechanisms.<n>Experiments can not be conducted directly on the target variables of interest, but are indirect.<n>We develop an adaptive strategy to design indirect experiments that optimally inform a targeted query about the ground truth mechanism.
arXiv Detail & Related papers (2024-05-30T12:14:25Z) - Disentangled Representation for Causal Mediation Analysis [25.114619307838602]
Causal mediation analysis is a method that is often used to reveal direct and indirect effects.
Deep learning shows promise in mediation analysis, but the current methods only assume latent confounders that affect treatment, mediator and outcome simultaneously.
We propose the Disentangled Mediation Analysis Variational AutoEncoder (DMAVAE), which disentangles the representations of latent confounders into three types to accurately estimate the natural direct effect, natural indirect effect and total effect.
arXiv Detail & Related papers (2023-02-19T23:37:17Z) - Neighborhood Adaptive Estimators for Causal Inference under Network Interference [109.17155002599978]
We consider the violation of the classical no-interference assumption with units connected by a network.<n>For tractability, we consider a known network that describes how interference may spread.
arXiv Detail & Related papers (2022-12-07T14:53:47Z) - Causal Ordering Without Effect Estimation: A Framework for Using Proxies in Treatment Prioritization [3.0509197593879844]
We develop a decision-focused framework to reason about predictive proxies.<n>We identify conditions under which proxies recover the correct effect ordering, which hold when a proxy reflects a dominant moderator of treatment effects.<n>We show how these conditions emerge as a useful approximation in discrete choice settings, where the propensity to act without an intervention moderates persuasion.
arXiv Detail & Related papers (2022-06-25T02:15:22Z) - Loss Bounds for Approximate Influence-Based Abstraction [81.13024471616417]
Influence-based abstraction aims to gain leverage by modeling local subproblems together with the 'influence' that the rest of the system exerts on them.
This paper investigates the performance of such approaches from a theoretical perspective.
We show that neural networks trained with cross entropy are well suited to learn approximate influence representations.
arXiv Detail & Related papers (2020-11-03T15:33:10Z) - A Class of Algorithms for General Instrumental Variable Models [29.558215059892206]
Causal treatment effect estimation is a key problem that arises in a variety of real-world settings.
We provide a method for causal effect bounding in continuous distributions.
arXiv Detail & Related papers (2020-06-11T12:32:24Z)
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.