Predicting perturbation targets with causal differential networks

• URL: http://arxiv.org/abs/2410.03380v1
• Date: Fri, 4 Oct 2024 12:48:21 GMT
• Title: Predicting perturbation targets with causal differential networks
• Authors: Menghua Wu, Umesh Padia, Sean H. Murphy, Regina Barzilay, Tommi Jaakkola,
• Abstract summary: We use an amortized causal discovery model to infer causal graphs from the observational and interventional datasets. We learn to map these paired graphs to the sets of variables that were intervened upon, in a supervised learning framework. This approach consistently outperforms baselines for perturbation modeling on seven single-cell transcriptomics datasets.
• Score: 23.568795598997376
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Rationally identifying variables responsible for changes to a biological system can enable myriad applications in disease understanding and cell engineering. From a causality perspective, we are given two datasets generated by the same causal model, one observational (control) and one interventional (perturbed). The goal is to isolate the subset of measured variables (e.g. genes) that were the targets of the intervention, i.e. those whose conditional independencies have changed. Knowing the causal graph would limit the search space, allowing us to efficiently pinpoint these variables. However, current algorithms that infer causal graphs in the presence of unknown intervention targets scale poorly to the hundreds or thousands of variables in biological data, as they must jointly search the combinatorial spaces of graphs and consistent intervention targets. In this work, we propose a causality-inspired approach for predicting perturbation targets that decouples the two search steps. First, we use an amortized causal discovery model to separately infer causal graphs from the observational and interventional datasets. Then, we learn to map these paired graphs to the sets of variables that were intervened upon, in a supervised learning framework. This approach consistently outperforms baselines for perturbation modeling on seven single-cell transcriptomics datasets, each with thousands of measured variables. We also demonstrate significant improvements over six causal discovery algorithms in predicting intervention targets across a variety of tractable, synthetic datasets.

Related papers

• Learning Mixtures of Unknown Causal Interventions [14.788930098406027]
  We consider the challenge of disentangling mixed interventional and observational data within Structural Equation Models (SEMs) We demonstrate that conducting interventions, whether do or soft, yields distributions with sufficient diversity and properties to efficiently recovering each component within the mixture. As a result, the causal graph can be identified up to its interventional Markov Equivalence Class, similar to scenarios where no noise influences the generation of interventional data.
  arXiv  Detail & Related papers  (2024-10-31T21:25:11Z)
• Sample Efficient Bayesian Learning of Causal Graphs from Interventions [6.823521786512908]
  This study considers a Bayesian approach for learning causal graphs with limited interventional samples. We show theoretically that our proposed algorithm will return the true causal graph with high probability. We present a case study showing how this algorithm could be modified to answer more general causal questions without learning the whole graph.
  arXiv  Detail & Related papers  (2024-10-26T05:47:56Z)
• Targeted Cause Discovery with Data-Driven Learning [66.86881771339145]
  We propose a novel machine learning approach for inferring causal variables of a target variable from observations. We employ a neural network trained to identify causality through supervised learning on simulated data. Empirical results demonstrate the effectiveness of our method in identifying causal relationships within large-scale gene regulatory networks.
  arXiv  Detail & Related papers  (2024-08-29T02:21:11Z)
• Adaptive Online Experimental Design for Causal Discovery [9.447864414136905]
  Causal discovery aims to uncover cause-and-effect relationships encoded in causal graphs. We focus on data interventional efficiency and formalize causal discovery from the perspective of online learning. We propose a track-and-stop causal discovery algorithm that adaptively selects interventions from the graph separating system.
  arXiv  Detail & Related papers  (2024-05-19T13:26:33Z)
• Sample, estimate, aggregate: A recipe for causal discovery foundation models [28.116832159265964]
  Causal discovery has the potential to uncover mechanistic insights from biological experiments. We propose a supervised model trained on large-scale, synthetic data to predict causal graphs. Our approach is enabled by the observation that typical errors in the outputs of a discovery algorithm remain comparable across datasets.
  arXiv  Detail & Related papers  (2024-02-02T21:57:58Z)
• Identifiability Guarantees for Causal Disentanglement from Soft Interventions [26.435199501882806]
  Causal disentanglement aims to uncover a representation of data using latent variables that are interrelated through a causal model. In this paper, we focus on the scenario where unpaired observational and interventional data are available, with each intervention changing the mechanism of a latent variable. When the causal variables are fully observed, statistically consistent algorithms have been developed to identify the causal model under faithfulness assumptions.
  arXiv  Detail & Related papers  (2023-07-12T15:39:39Z)
• 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)
• Nonparametric Identifiability of Causal Representations from Unknown Interventions [63.1354734978244]
  We study causal representation learning, the task of inferring latent causal variables and their causal relations from mixtures of the variables. Our goal is to identify both the ground truth latents and their causal graph up to a set of ambiguities which we show to be irresolvable from interventional data.
  arXiv  Detail & Related papers  (2023-06-01T10:51:58Z)
• Large-Scale Differentiable Causal Discovery of Factor Graphs [3.8015092217142223]
  We introduce the notion of factor directed acyclic graphs (f-DAGs) as a way to the search space to non-linear low-rank causal interaction models. We propose a scalable implementation of f-DAG constrained causal discovery for high-dimensional interventional data.
  arXiv  Detail & Related papers  (2022-06-15T21:28:36Z)
• The interventional Bayesian Gaussian equivalent score for Bayesian causal inference with unknown soft interventions [0.0]
  In certain settings, such as genomics, we may have data from heterogeneous study conditions, with soft (partial) interventions only pertaining to a subset of the study variables. We define the interventional BGe score for a mixture of observational and interventional data, where the targets and effects of intervention may be unknown.
  arXiv  Detail & Related papers  (2022-05-05T12:32:08Z)
• Scalable Intervention Target Estimation in Linear Models [52.60799340056917]
  Current approaches to causal structure learning either work with known intervention targets or use hypothesis testing to discover the unknown intervention targets. This paper proposes a scalable and efficient algorithm that consistently identifies all intervention targets. The proposed algorithm can be used to also update a given observational Markov equivalence class into the interventional Markov equivalence class.
  arXiv  Detail & Related papers  (2021-11-15T03:16:56Z)
• Learning Neural Causal Models with Active Interventions [83.44636110899742]
  We introduce an active intervention-targeting mechanism which enables a quick identification of the underlying causal structure of the data-generating process. Our method significantly reduces the required number of interactions compared with random intervention targeting. We demonstrate superior performance on multiple benchmarks from simulated to real-world data.
  arXiv  Detail & Related papers  (2021-09-06T13:10:37Z)
• Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning [76.00395335702572]
  A central goal for AI and causality is the joint discovery of abstract representations and causal structure. Existing environments for studying causal induction are poorly suited for this objective because they have complicated task-specific causal graphs. In this work, our goal is to facilitate research in learning representations of high-level variables as well as causal structures among them.
  arXiv  Detail & Related papers  (2021-07-02T05:44:56Z)
• 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 Discovery in Physical Systems from Videos [123.79211190669821]
  Causal discovery is at the core of human cognition. We consider the task of causal discovery from videos in an end-to-end fashion without supervision on the ground-truth graph structure.
  arXiv  Detail & Related papers  (2020-07-01T17:29:57Z)

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.