The Determination of the Equivalence of Causal Theories

Wang, Yu; Zhang, Jinjin

doi:10.1145/3457682.3457757

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…Fig. 3 presents the rank-six CPDAG model obtained from our algorithm and compares it to the estimates obtained from the literature (25)(26)(27). Our CPDAG estimate has fewer edges than those in refs.…”

Section: Methodsmentioning

confidence: 95%

Model selection over partially ordered sets

Taeb,

Bühlmann,

Chandrasekaran

2024

Proc. Natl. Acad. Sci. U.S.A.

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In problems such as variable selection and graph estimation, models are characterized by Boolean logical structure such as the presence or absence of a variable or an edge. Consequently, false-positive error or false-negative error can be specified as the number of variables/edges that are incorrectly included or excluded in an estimated model. However, there are several other problems such as ranking, clustering, and causal inference in which the associated model classes do not admit transparent notions of false-positive and false-negative errors due to the lack of an underlying Boolean logical structure. In this paper, we present a generic approach to endow a collection of models with partial order structure, which leads to a hierarchical organization of model classes as well as natural analogs of false-positive and false-negative errors. We describe model selection procedures that provide false-positive error control in our general setting, and we illustrate their utility with numerical experiments.

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Section: Methodsmentioning

confidence: 95%

Model selection over partially ordered sets

Taeb,

Bühlmann,

Chandrasekaran

2024

Proc. Natl. Acad. Sci. U.S.A.

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“…Several prior works have also looked to characterize a narrower equivalence class by leveraging interventional data [Eberhardt and Scheines, 2007, He and Geng, 2008, Hauser and Bühlmann, 2012, Yang et al, 2018.…”

Section: Causal Discoverymentioning

confidence: 99%

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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The Determination of the Equivalence of Causal Theories

Cited by 2 publications

References 8 publications

Model selection over partially ordered sets

Model selection over partially ordered sets

Discovering Optimal Scoring Mechanisms in Causal Strategic Prediction

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