Estimating Direct and Indirect Causal Effects of Spatiotemporal Interventions in Presence of Spatial Interference

• URL: http://arxiv.org/abs/2405.08174v2
• Date: Thu, 29 Aug 2024 23:21:03 GMT
• Title: Estimating Direct and Indirect Causal Effects of Spatiotemporal Interventions in Presence of Spatial Interference
• Authors: Sahara Ali, Omar Faruque, Jianwu Wang,
• Abstract summary: We first extend the concept of spatial interference in case of time-varying treatment outcomes by extending the potential outcome framework under the assumption of no unmeasured confounding. We then propose our deep learning based potential outcome model fortemporal causal inference. We utilize latent factor modeling to reduce interference due to time-varying confounding while leveraging the power of U-Net architecture to capture global spatial interference in data over time.
• Score: 0.46052594866569146
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Spatial interference (SI) occurs when the treatment at one location affects the outcomes at other locations. Accounting for spatial interference in spatiotemporal settings poses further challenges as interference violates the stable unit treatment value assumption, making it infeasible for standard causal inference methods to quantify the effects of time-varying treatment at spatially varying outcomes. In this paper, we first formalize the concept of spatial interference in case of time-varying treatment assignments by extending the potential outcome framework under the assumption of no unmeasured confounding. We then propose our deep learning based potential outcome model for spatiotemporal causal inference. We utilize latent factor modeling to reduce the bias due to time-varying confounding while leveraging the power of U-Net architecture to capture global and local spatial interference in data over time. Our causal estimators are an extension of average treatment effect (ATE) for estimating direct (DATE) and indirect effects (IATE) of spatial interference on treated and untreated data. Being the first of its kind deep learning based spatiotemporal causal inference technique, our approach shows advantages over several baseline methods based on the experiment results on two synthetic datasets, with and without spatial interference. Our results on real-world climate dataset also align with domain knowledge, further demonstrating the effectiveness of our proposed method.

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)
• A Practical Approach to Causal Inference over Time [17.660953125689105]
  We define causal interventions and their effects over time on discrete-time processes (DSPs) We show under which conditions the equilibrium states of a DSP, both before and after a causal intervention, can be captured by a structural causal model (SCM) The resulting causal VAR framework allows us to perform causal inference over time from observational time series data.
  arXiv  Detail & Related papers  (2024-10-14T13:45:20Z)
• On the Identification of Temporally Causal Representation with Instantaneous Dependence [50.14432597910128]
  Temporally causal representation learning aims to identify the latent causal process from time series observations. Most methods require the assumption that the latent causal processes do not have instantaneous relations. We propose an textbfIDentification framework for instantanetextbfOus textbfLatent dynamics.
  arXiv  Detail & Related papers  (2024-05-24T08:08:05Z)
• 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)
• 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)
• 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)
• Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations [84.42837346400151]
  Estimating counterfactual outcomes over time has the potential to unlock personalized healthcare. Existing causal inference approaches consider regular, discrete-time intervals between observations and treatment decisions. We propose a controllable simulation environment based on a model of tumor growth for a range of scenarios.
  arXiv  Detail & Related papers  (2022-06-16T17:15:15Z)
• SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data [83.50281440043241]
  We study the problem of inferring heterogeneous treatment effects from time-to-event data. We propose a novel deep learning method for treatment-specific hazard estimation based on balancing representations.
  arXiv  Detail & Related papers  (2021-10-26T20:13:17Z)
• 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)
• Causal Inference under Networked Interference and Intervention Policy Enhancement [35.149125599812706]
  Estimating individual treatment effects from data of randomized experiments is a critical task in causal inference. Usually, in randomized experiments or observational studies with interconnected units, one can only observe treatment responses under interference. We study causal effect estimation under general network interference using GNNs, which are powerful tools for capturing the dependency in the graph.
  arXiv  Detail & Related papers  (2020-02-20T00:35:50Z)

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.