Large-Scale Targeted Cause Discovery with Data-Driven Learning

• URL: http://arxiv.org/abs/2408.16218v2
• Date: Mon, 07 Apr 2025 06:11:00 GMT
• Title: Large-Scale Targeted Cause Discovery with Data-Driven Learning
• Authors: Jang-Hyun Kim, Claudia Skok Gibbs, Sangdoo Yun, Hyun Oh Song, Kyunghyun Cho,
• Abstract summary: We propose a novel machine learning approach for inferring causal variables of a target variable from observations.<n>By employing a local-inference strategy, our approach scales with linear complexity in the number of variables, efficiently scaling up to thousands of variables.<n> Empirical results demonstrate superior performance in identifying causal relationships within large-scale gene regulatory networks.
• Score: 66.86881771339145
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a novel machine learning approach for inferring causal variables of a target variable from observations. Our focus is on directly inferring a set of causal factors without requiring full causal graph reconstruction, which is computationally challenging in large-scale systems. The identified causal set consists of all potential regulators of the target variable under experimental settings, enabling efficient regulation when intervention costs and feasibility vary across variables. To achieve this, we train a neural network using supervised learning on simulated data to infer causality. By employing a local-inference strategy, our approach scales with linear complexity in the number of variables, efficiently scaling up to thousands of variables. Empirical results demonstrate superior performance in identifying causal relationships within large-scale gene regulatory networks, outperforming existing methods that emphasize full-graph discovery. We validate our model's generalization capability across out-of-distribution graph structures and generating mechanisms, including gene regulatory networks of E. coli and the human K562 cell line. Implementation codes are available at https://github.com/snu-mllab/Targeted-Cause-Discovery.

Related papers

• Predicting perturbation targets with causal differential networks [23.568795598997376]
  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.
  arXiv  Detail & Related papers  (2024-10-04T12:48:21Z)
• Generative Principal Component Regression via Variational Inference [2.4415762506639944]
  One approach to designing appropriate manipulations is to target key features of predictive models. We develop a novel objective based on supervised variational autoencoders (SVAEs) that enforces such information is represented in the latent space. We show in simulations that gPCR dramatically improves target selection in manipulation as compared to standard PCR and SVAEs.
  arXiv  Detail & Related papers  (2024-09-03T22:38:55Z)
• Multi-modal Causal Structure Learning and Root Cause Analysis [67.67578590390907]
  We propose Mulan, a unified multi-modal causal structure learning method for root cause localization. We leverage a log-tailored language model to facilitate log representation learning, converting log sequences into time-series data. We also introduce a novel key performance indicator-aware attention mechanism for assessing modality reliability and co-learning a final causal graph.
  arXiv  Detail & Related papers  (2024-02-04T05:50:38Z)
• 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)
• Variable Importance in High-Dimensional Settings Requires Grouping [19.095605415846187]
  Conditional Permutation Importance (CPI) bypasses PI's limitations in such cases. Grouping variables statistically via clustering or some prior knowledge gains some power back. We show that the approach extended with stacking controls the type-I error even with highly-correlated groups.
  arXiv  Detail & Related papers  (2023-12-18T00:21:47Z)
• 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)
• Structural restrictions in local causal discovery: identifying direct causes of a target variable [0.9208007322096533]
  Learning a set of direct causes of a target variable from an observational joint distribution is a fundamental problem in science. Here, we are only interested in identifying the direct causes of one target variable, not the full DAG. This allows us to relax the identifiability assumptions and develop possibly faster and more robust algorithms.
  arXiv  Detail & Related papers  (2023-07-29T18:31:35Z)
• Amortized Inference for Causal Structure Learning [72.84105256353801]
  Learning causal structure poses a search problem that typically involves evaluating structures using a score or independence test. We train a variational inference model to predict the causal structure from observational/interventional data. Our models exhibit robust generalization capabilities under substantial distribution shift.
  arXiv  Detail & Related papers  (2022-05-25T17:37:08Z)
• VICause: Simultaneous Missing Value Imputation and Causal Discovery with Groups [12.055670392677248]
  We propose VICause, a novel approach to tackle missing value imputation and causal discovery efficiently with deep learning. We show improved performance compared to popular and recent approaches in both missing value imputation and causal discovery.
  arXiv  Detail & Related papers  (2021-10-15T17:35:20Z)
• 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)
• The Causal Neural Connection: Expressiveness, Learnability, and Inference [125.57815987218756]
  An object called structural causal model (SCM) represents a collection of mechanisms and sources of random variation of the system under investigation. In this paper, we show that the causal hierarchy theorem (Thm. 1, Bareinboim et al., 2020) still holds for neural models. We introduce a special type of SCM called a neural causal model (NCM), and formalize a new type of inductive bias to encode structural constraints necessary for performing causal inferences.
  arXiv  Detail & Related papers  (2021-07-02T01:55:18Z)
• Understanding Overparameterization in Generative Adversarial Networks [56.57403335510056]
  Generative Adversarial Networks (GANs) are used to train non- concave mini-max optimization problems. A theory has shown the importance of the gradient descent (GD) to globally optimal solutions. We show that in an overized GAN with a $1$-layer neural network generator and a linear discriminator, the GDA converges to a global saddle point of the underlying non- concave min-max problem.
  arXiv  Detail & Related papers  (2021-04-12T16:23:37Z)
• A Trainable Optimal Transport Embedding for Feature Aggregation and its Relationship to Attention [96.77554122595578]
  We introduce a parametrized representation of fixed size, which embeds and then aggregates elements from a given input set according to the optimal transport plan between the set and a trainable reference. Our approach scales to large datasets and allows end-to-end training of the reference, while also providing a simple unsupervised learning mechanism with small computational cost.
  arXiv  Detail & Related papers  (2020-06-22T08:35:58Z)

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.