arXiv subjects

Karen Sachs

Publications and source records attributed to Karen Sachs.

4 recordsLinked to original sources

arXiv2025

The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applications

Causal discovery aims to automatically uncover causal relationships from data, a capability with significant potential across many scientific disciplines. However, its real-world applications remain limited. Current methods often rely on unrealistic assumptions and are evaluated only on simple synthetic toy datasets, often with inadequate evaluation metrics. In this paper, we substantiate these claims by performing a systematic review of the recent causal discovery literature. We present applications in biology, neuroscience, and Earth sciences - fields where causal discovery holds promise for addressing key challenges. We highlight available simulated and real-world datasets from these domains and discuss common assumption violations that have spurred the development of new methods. Our goal is to encourage the community to adopt better evaluation practices by utilizing realistic datasets and more adequate metrics.

cs.LG↗

arXiv2022

Experimental design for causal query estimation in partially observed biomolecular networks

Estimating a causal query from observational data is an essential task in the analysis of biomolecular networks. Estimation takes as input a network topology, a query estimation method, and observational measurements on the network variables. However, estimations involving many variables can be experimentally expensive, and computationally intractable. Moreover, using the full set of variables can be detrimental, leading to bias, or increasing the variance in the estimation. Therefore, designing an experiment based on a well-chosen subset of network components can increase estimation accuracy, and reduce experimental and computational costs. We propose a simulation-based algorithm for selecting sub-networks that support unbiased estimators of the causal query under a constraint of cost, ranked with respect to the variance of the estimators. The simulations are constructed based on historical experimental data, or based on known properties of the biological system. Three case studies demonstrated the effectiveness of well-chosen network subsets for estimating causal queries from observational data. All the case studies are reproducible and available at https://github.com/srtaheri/Simplified_LVM.

q-bio.BM↗

arXiv2022

Do-calculus enables estimation of causal effects in partially observed biomolecular pathways

Estimating causal queries, such as changes in protein abundance in response to a perturbation, is a fundamental task in the analysis of biomolecular pathways. The estimation requires experimental measurements on the pathway components. However, in practice many pathway components are left unobserved (latent) because they are either unknown, or difficult to measure. Latent variable models (LVMs) are well-suited for such estimation. Unfortunately, LVM-based estimation of causal queries can be inaccurate when parameters of the latent variables are not uniquely identified, or when the number of latent variables is misspecified. This has limited the use of LVMs for causal inference in biomolecular pathways. In this manuscript, we propose a general and practical approach for LVM-based estimation of causal queries. We prove that, despite the challenges above, LVM-based estimators of causal queries are accurate if the queries are identifiable according to Pearl's do-calculus, and describe an algorithm for its estimation. We illustrate the breadth and the practical utility of this approach for estimating causal queries in four synthetic and two experimental case studies, where structures of biomolecular pathways challenge the existing methods for causal query estimation. The code and the data documenting all the case studies are available at \url{https://github.com/srtaheri/LVMwithDoCalculus}

cs.LG↗

arXiv2017

Graph-Sparse Logistic Regression

We introduce Graph-Sparse Logistic Regression, a new algorithm for classification for the case in which the support should be sparse but connected on a graph. We val- idate this algorithm against synthetic data and benchmark it against L1-regularized Logistic Regression. We then explore our technique in the bioinformatics context of proteomics data on the interactome graph. We make all our experimental code public and provide GSLR as an open source package.

cs.LG↗