Causal Structure Learning: A Combinatorial Perspective

Squires, Chandler; Uhler, Caroline

doi:10.1007/s10208-022-09581-9

Cited by 25 publications

(18 citation statements)

References 96 publications

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“…In the potential outcome framework, the key difficulty for general unsupervised causal inference is the mixing of confounders with outcomes. In the field of causal inference, such a case is described as learning with both interventions and latent confounding, which remains an active area of research [27]. In our case, a gene can contribute to confounding variation as well as treatment-associated variation.…”

Section: Resultsmentioning

confidence: 99%

Causal identification of single-cell experimental perturbation effects with CINEMA-OT

Dong

Wang

Wei

et al. 2022

Preprint

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Recent advancements in single-cell technologies allow characterization of experimental perturbations at single-cell resolution. While methods have been developed to analyze data from such experiments, the application of a strict causal framework has not yet been explored for the inference of treatment effects at the single-cell level. In this work, we present a causal inference based approach to single-cell perturbation analysis, termed CINEMA-OT (Causal INdependent Effect Module Attribution + Optimal Transport). CINEMA-OT separates confounding sources of variation from perturbation effects to obtain an optimal transport matching that reflects counterfactual cell pairs. These cell pairs represent causal perturbation responses permitting a number of novel analyses, such as individual treatment effect analysis, response clustering, attribution analysis, and synergy analysis. We benchmark CINEMA-OT on an array of treatment effect estimation tasks for several simulated and real datasets and show that it outperforms other single-cell perturbation analysis methods. Finally, we perform CINEMA-OT analysis of two newly-generated datasets: (1) rhinovirus-infected airway organoids, and (2) combinatorial cytokine stimulation of immune cells. Using CINEMA-OT, we discover diverging treatment responses and their associated cellular sub-populations. By applying CINEMA-OT to combinatorial experimental designs, we infer the specific cell-gene programs driving syngergistic responses.

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Section: Resultsmentioning

confidence: 99%

Causal identification of single-cell experimental perturbation effects with CINEMA-OT

Dong

Wang

Wei

et al. 2022

Preprint

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“…(c) Causal representation learning [108], where a latent representation of the system is learnt, and causal structure within this representation, rather than the entire system, is inferred. For example, latent factor causal models [109] model gene regulatory networks through the interaction of unobserved latent factors that cluster genes. (d ) The components of a gene regulatory network can be split into modules or gene programmes which can then themselves be studied, for example [110] and [111].…”

Section: Discussionmentioning

confidence: 99%

A dynamical perspective: moving towards mechanism in single-cell transcriptomics

Maizels

2024

Phil. Trans. R. Soc. B

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As the field of single-cell transcriptomics matures, research is shifting focus from phenomenological descriptions of cellular phenotypes to a mechanistic understanding of the gene regulation underneath. This perspective considers the value of capturing dynamical information at single-cell resolution for gaining mechanistic insight; reviews the available technologies for recording and inferring temporal information in single cells; and explores whether better dynamical resolution is sufficient to adequately capture the causal relationships driving complex biological systems. This article is part of a discussion meeting issue ‘Causes and consequences of stochastic processes in development and disease’.

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“…Other works have characterized the interventional equivalence class when the targets of the interventions are unknown [Jaber et al, 2020, Squires et al, 2020. By contrast, in our setting, we discover the underlying graph in cases where soft interventions are carried out at unknown targets by individuals in response to the deployed scoring mechanisms.…”

Section: Causal Discoverymentioning

confidence: 94%

Discovering Optimal Scoring Mechanisms in Causal Strategic Prediction

Yan¹,

Gupta²

2023

Preprint

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Faced with data-driven policies, individuals will manipulate their features to obtain favorable decisions. While earlier works cast these manipulations as undesirable gaming, recent works have adopted a more nuanced causal framing in which manipulations can improve outcomes of interest, and setting coherent mechanisms requires accounting for both predictive accuracy and improvement of the outcome. Typically, these works focus on known causal graphs, consisting only of an outcome and its parents. In this paper, we introduce a general framework in which an outcome and observed features are related by an arbitrary unknown graph and manipulations are restricted by a xed budget and cost structure. We develop algorithms that leverage strategic responses to discover the causal graph in a nite number of steps. Given this graph structure, we can then derive mechanisms that trade o between accuracy and improvement. Altogether, our work deepens links between causal discovery and incentive design and provides a more nuanced view of learning under causal strategic prediction.

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Causal Structure Learning: A Combinatorial Perspective

Cited by 25 publications

References 96 publications

Causal identification of single-cell experimental perturbation effects with CINEMA-OT

Causal identification of single-cell experimental perturbation effects with CINEMA-OT

A dynamical perspective: moving towards mechanism in single-cell transcriptomics

Discovering Optimal Scoring Mechanisms in Causal Strategic Prediction

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