Related papers: Trust Your $\nabla$: Gradient-based Intervention Targeting for Causal Discovery

Trust Your $\nabla$: Gradient-based Intervention Targeting for Causal Discovery

URL: http://arxiv.org/abs/2211.13715v5
Date: Wed, 3 Apr 2024 16:03:35 GMT
Title: Trust Your $\nabla$: Gradient-based Intervention Targeting for Causal Discovery
Authors: Mateusz Olko, Michał Zając, Aleksandra Nowak, Nino Scherrer, Yashas Annadani, Stefan Bauer, Łukasz Kuciński, Piotr Miłoś,
Abstract summary: In this work, we propose a novel Gradient-based Intervention Targeting method, abbreviated GIT. GIT 'trusts' the gradient estimator of a gradient-based causal discovery framework to provide signals for the intervention acquisition function. We provide extensive experiments in simulated and real-world datasets and demonstrate that GIT performs on par with competitive baselines.
Score: 49.084423861263524
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Inferring causal structure from data is a challenging task of fundamental importance in science. Observational data are often insufficient to identify a system's causal structure uniquely. While conducting interventions (i.e., experiments) can improve the identifiability, such samples are usually challenging and expensive to obtain. Hence, experimental design approaches for causal discovery aim to minimize the number of interventions by estimating the most informative intervention target. In this work, we propose a novel Gradient-based Intervention Targeting method, abbreviated GIT, that 'trusts' the gradient estimator of a gradient-based causal discovery framework to provide signals for the intervention acquisition function. We provide extensive experiments in simulated and real-world datasets and demonstrate that GIT performs on par with competitive baselines, surpassing them in the low-data regime.

Related papers

CAnDOIT: Causal Discovery with Observational and Interventional Data from Time-Series [4.008958683836471]
CAnDOIT is a causal discovery method to reconstruct causal models using both observational and interventional data. The use of interventional data in the causal analysis is crucial for real-world applications, such as robotics. A Python implementation of CAnDOIT has also been developed and is publicly available on GitHub.
arXiv Detail & Related papers (2024-10-03T13:57:08Z)
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)
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)
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)
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)
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)
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)

This list is automatically generated from the titles and abstracts of the papers in this site.