Causal Structure Learning: a Combinatorial Perspective

Squires, Chandler; Uhler, Caroline

doi:10.48550/arxiv.2206.01152

Cited by 2 publications

(2 citation statements)

References 86 publications

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

Section: Confounder Signal Matching Via Cinema-otmentioning

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: Confounder Signal Matching Via Cinema-otmentioning

confidence: 99%

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

Dong

Wang

Wei

et al. 2022

Preprint

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show abstract

“…Causal discovery has been investigated under a variety of assumptions (Glymour et al, 2019;Squires and Uhler, 2022). Under causal sufficiency (i.e., no unobserved variables) and faithfulness, the PC algorithm (Spirtes et al, 2000, Sec.…”

Section: Introductionmentioning

confidence: 99%

Local Causal Discovery for Estimating Causal Effects

Gupta¹,

Childers²

2023

Preprint

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Even when the causal graph underlying our data is unknown, we can use observational data to narrow down the possible values that an average treatment effect (ATE) can take by (1) identifying the graph up to a Markov equivalence class; and (2) estimating that ATE for each graph in the class. While the PC algorithm can identify this class under strong faithfulness assumptions, it can be computationally prohibitive. Fortunately, only the local graph structure around the treatment is required to identify the set of possible ATE values, a fact exploited by local discovery algorithms to improve computational efficiency. In this paper, we introduce Local Discovery using Eager Collider Checks (LDECC), a new local causal discovery algorithm that leverages unshielded colliders to orient the treatment's parents differently from existing methods. We show that there exist graphs where LDECC exponentially outperforms existing local discovery algorithms and vice versa. Moreover, we show that LDECC and existing algorithms rely on different faithfulness assumptions, leveraging this insight to weaken the assumptions for identifying the set of possible ATE values.

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Causal Structure Learning: a Combinatorial Perspective

Cited by 2 publications

References 86 publications

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

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

Local Causal Discovery for Estimating Causal Effects

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