Causal Message Passing: A Method for Experiments with Unknown and General Network Interference

• URL: http://arxiv.org/abs/2311.08340v2
• Date: Thu, 27 Jun 2024 01:12:27 GMT
• Title: Causal Message Passing: A Method for Experiments with Unknown and General Network Interference
• Authors: Sadegh Shirani, Mohsen Bayati,
• Abstract summary: 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.
• Score: 5.294604210205507
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Randomized experiments are a powerful methodology for data-driven evaluation of decisions or interventions. Yet, their validity may be undermined by network interference. This occurs when the treatment of one unit impacts not only its outcome but also that of connected units, biasing traditional treatment effect estimations. Our study introduces a new framework to accommodate complex and unknown network interference, moving beyond specialized models in the existing literature. Our framework, termed causal message-passing, is grounded in high-dimensional approximate message passing methodology. It is tailored for multi-period experiments and is particularly effective in settings with many units and prevalent network interference. The framework models causal effects as a dynamic process where a treated unit's impact propagates through the network via neighboring units until equilibrium is reached. This approach allows us to approximate the dynamics of potential outcomes over time, enabling the extraction of valuable information before treatment effects reach equilibrium. Utilizing causal message-passing, we introduce a practical algorithm to estimate the total treatment effect, defined as the impact observed when all units are treated compared to the scenario where no unit receives treatment. We demonstrate the effectiveness of this approach across five numerical scenarios, each characterized by a distinct interference structure.

Related papers

• Model-Based Inference and Experimental Design for Interference Using Partial Network Data [4.76518127830168]
  We present a framework for the estimation and inference of treatment effect adjustments using partial network data. We illustrate procedures to assign treatments using only partial network data. We validate our approach using simulated experiments on observed graphs with applications to information diffusion in India and Malawi.
  arXiv  Detail & Related papers  (2024-06-17T17:27:18Z)
• Integrating Active Learning in Causal Inference with Interference: A Novel Approach in Online Experiments [5.488412825534217]
  We introduce an active learning approach: Active Learning in Causal Inference with Interference (ACI) ACI uses Gaussian process to flexibly model the direct and spillover treatment effects as a function of a continuous measure of neighbors' treatment assignment. We demonstrate its feasibility in achieving accurate effects estimations with reduced data requirements.
  arXiv  Detail & Related papers  (2024-02-20T04:13:59Z)
• A Reinforcement Learning Framework for Dynamic Mediation Analysis [16.284199152492487]
  We propose a reinforcement learning framework to evaluate dynamic mediation effects in settings with infinite horizons. We decompose the average treatment effect into an immediate direct effect, an immediate mediation effect, a delayed direct effect, and a delayed mediation effect. We develop robust and semi-parametrically efficient estimators under the RL framework to infer these causal effects.
  arXiv  Detail & Related papers  (2023-01-31T00:50:05Z)
• TCFimt: Temporal Counterfactual Forecasting from Individual Multiple Treatment Perspective [50.675845725806724]
  We propose a comprehensive framework of temporal counterfactual forecasting from an individual multiple treatment perspective (TCFimt) TCFimt constructs adversarial tasks in a seq2seq framework to alleviate selection and time-varying bias and designs a contrastive learning-based block to decouple a mixed treatment effect into separated main treatment effects and causal interactions. The proposed method shows satisfactory performance in predicting future outcomes with specific treatments and in choosing optimal treatment type and timing than state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-12-17T15:01:05Z)
• Neighborhood Adaptive Estimators for Causal Inference under Network Interference [152.4519491244279]
  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.
  arXiv  Detail & Related papers  (2022-12-07T14:53:47Z)
• CausalBench: A Large-scale Benchmark for Network Inference from Single-cell Perturbation Data [61.088705993848606]
  We introduce CausalBench, a benchmark suite for evaluating causal inference methods on real-world interventional data. CaulBench incorporates biologically-motivated performance metrics, including new distribution-based interventional metrics.
  arXiv  Detail & Related papers  (2022-10-31T13:04:07Z)
• Learning Individual Treatment Effects under Heterogeneous Interference in Networks [34.16062968227468]
  Estimates of individual treatment effects from networked observational data are attracting increasing attention. One major challenge in network scenarios is the violation of the stable unit treatment value assumption. We propose a novel Dual Weighting Regression (DWR) algorithm by simultaneously learning attention weights.
  arXiv  Detail & Related papers  (2022-10-25T15:00:05Z)
• Fair Effect Attribution in Parallel Online Experiments [57.13281584606437]
  A/B tests serve the purpose of reliably identifying the effect of changes introduced in online services. It is common for online platforms to run a large number of simultaneous experiments by splitting incoming user traffic randomly. Despite a perfect randomization between different groups, simultaneous experiments can interact with each other and create a negative impact on average population outcomes.
  arXiv  Detail & Related papers  (2022-10-15T17:15:51Z)
• 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)
• Nonparametric inference for interventional effects with multiple mediators [0.0]
  We provide theory that allows for more flexible, possibly machine learning-based, estimation techniques. We demonstrate multiple robustness properties of the proposed estimators. Our work thus provides a means of leveraging modern statistical learning techniques in estimation of interventional mediation effects.
  arXiv  Detail & Related papers  (2020-01-16T19:05:00Z)

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.