Targeted Sequential Indirect Experiment Design

• URL: http://arxiv.org/abs/2405.19985v1
• Date: Thu, 30 May 2024 12:14:25 GMT
• Title: Targeted Sequential Indirect Experiment Design
• Authors: Elisabeth Ailer, Niclas Dern, Jason Hartford, Niki Kilbertus,
• Abstract summary: hypotheses concern specific aspects of complex, imperfectly understood or entirely unknown mechanisms. Experiments can not be conducted directly on the target variables of interest, but are indirect. We develop an adaptive strategy to design indirect experiments that optimally inform a targeted query about the ground truth mechanism.
• Score: 4.262342157729123
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Scientific hypotheses typically concern specific aspects of complex, imperfectly understood or entirely unknown mechanisms, such as the effect of gene expression levels on phenotypes or how microbial communities influence environmental health. Such queries are inherently causal (rather than purely associational), but in many settings, experiments can not be conducted directly on the target variables of interest, but are indirect. Therefore, they perturb the target variable, but do not remove potential confounding factors. If, additionally, the resulting experimental measurements are multi-dimensional and the studied mechanisms nonlinear, the query of interest is generally not identified. We develop an adaptive strategy to design indirect experiments that optimally inform a targeted query about the ground truth mechanism in terms of sequentially narrowing the gap between an upper and lower bound on the query. While the general formulation consists of a bi-level optimization procedure, we derive an efficiently estimable analytical kernel-based estimator of the bounds for the causal effect, a query of key interest, and demonstrate the efficacy of our approach in confounded, multivariate, nonlinear synthetic settings.

Related papers

• Double Machine Learning meets Panel Data -- Promises, Pitfalls, and Potential Solutions [0.0]
  Estimating causal effect using machine learning (ML) algorithms can help to relax functional form assumptions if used within appropriate frameworks. We show how we can adapt machine learning (DML) for panel data in the presence of unobserved heterogeneity. We also show that the influence of the unobserved heterogeneity on the observed confounders plays a significant role for the performance of most alternative methods.
  arXiv  Detail & Related papers  (2024-09-02T13:59:54Z)
• DiscoBAX: Discovery of Optimal Intervention Sets in Genomic Experiment Design [61.48963555382729]
  We propose DiscoBAX as a sample-efficient method for maximizing the rate of significant discoveries per experiment. We provide theoretical guarantees of approximate optimality under standard assumptions, and conduct a comprehensive experimental evaluation.
  arXiv  Detail & Related papers  (2023-12-07T06:05:39Z)
• Causal machine learning for single-cell genomics [94.28105176231739]
  We discuss the application of machine learning techniques to single-cell genomics and their challenges. We first present the model that underlies most of current causal approaches to single-cell biology. We then identify open problems in the application of causal approaches to single-cell data.
  arXiv  Detail & Related papers  (2023-10-23T13:35:24Z)
• A Causal Framework for Decomposing Spurious Variations [68.12191782657437]
  We develop tools for decomposing spurious variations in Markovian and Semi-Markovian models. We prove the first results that allow a non-parametric decomposition of spurious effects. The described approach has several applications, ranging from explainable and fair AI to questions in epidemiology and medicine.
  arXiv  Detail & Related papers  (2023-06-08T09:40:28Z)
• 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)
• Active Learning for Optimal Intervention Design in Causal Models [11.294389953686945]
  We develop a causal active learning strategy to identify interventions that are optimal, as measured by the discrepancy between the post-interventional mean of the distribution and a desired target mean. We apply our approach to both synthetic data and single-cell transcriptomic data from Perturb-CITE-seq experiments to identify optimal perturbations that induce a specific cell state transition.
  arXiv  Detail & Related papers  (2022-09-10T20:40:30Z)
• BaCaDI: Bayesian Causal Discovery with Unknown Interventions [118.93754590721173]
  BaCaDI operates in the continuous space of latent probabilistic representations of both causal structures and interventions. In experiments on synthetic causal discovery tasks and simulated gene-expression data, BaCaDI outperforms related methods in identifying causal structures and intervention targets.
  arXiv  Detail & Related papers  (2022-06-03T16:25:48Z)
• Interventions, Where and How? Experimental Design for Causal Models at Scale [47.63842422086614]
  Causal discovery from observational and interventional data is challenging due to limited data and non-identifiability. In this paper, we incorporate recent advances in Bayesian causal discovery into the Bayesian optimal experimental design framework. We demonstrate the performance of the proposed method on synthetic graphs for both linear and nonlinear SCMs as well as on the in-silico single-cell gene regulatory network dataset, DREAM.
  arXiv  Detail & Related papers  (2022-03-03T20:59:04Z)
• Discovering Latent Causal Variables via Mechanism Sparsity: A New Principle for Nonlinear ICA [81.4991350761909]
  Independent component analysis (ICA) refers to an ensemble of methods which formalize this goal and provide estimation procedure for practical application. We show that the latent variables can be recovered up to a permutation if one regularizes the latent mechanisms to be sparse.
  arXiv  Detail & Related papers  (2021-07-21T14:22:14Z)

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.