February 18, 2024Open Access

Credible causal inference beyond toy models

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PBPablo Geraldo BastíasUniversity of Oxford

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Overview

Methodological review demonstrates how directed acyclic graphs improve empirical causal inference beyond rigid identification templates, highlighting gains in transparency and testability.

Key Points

Directed acyclic graphs enhance causal inference from observational data, exposing discrepancies between rigid identification strategies and practical empirical applications.
Methodological analysis of causal graphical models surveys literature arguments against research templates, examining empirical challenges across a series of structured worked examples.
Routinely incorporating causal graphical models supports transparent research design, enabling verifiable assumptions and greater credibility in observational causal claims.

Cite This Study

Pablo Geraldo Bastías (2024) studied this question.

synapsesocial.com/papers/68e78b99b6db6435876fdbdc https://doi.org/10.48550/arxiv.2402.11659

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February 18, 2024Open Access

Credible causal inference beyond toy models

View Full Paper

Ask AI

Bookmark

Authors

PBPablo Geraldo BastíasUniversity of Oxford

Discussion

Member takes

Overview

Methodological review demonstrates how directed acyclic graphs improve empirical causal inference beyond rigid identification templates, highlighting gains in transparency and testability.

Key Points

Directed acyclic graphs enhance causal inference from observational data, exposing discrepancies between rigid identification strategies and practical empirical applications.
Methodological analysis of causal graphical models surveys literature arguments against research templates, examining empirical challenges across a series of structured worked examples.
Routinely incorporating causal graphical models supports transparent research design, enabling verifiable assumptions and greater credibility in observational causal claims.

Cite This Study

Pablo Geraldo Bastías (2024) studied this question.

synapsesocial.com/papers/68e78b99b6db6435876fdbdc https://doi.org/10.48550/arxiv.2402.11659

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