词条 | Exploratory causal analysis |
释义 |
MotivationData analysis is primarily concerned with causal questions.[1][2][5][6][7] For example, did the fertilizer cause the crops to grow?[8] Or, can a given sickness be prevented?[9] Or, why is my friend depressed?[10] The potential outcomes and regression analysis techniques handle such queries when data is collected using designed experiments. Data collected in observational studies require different techniques for causal inference (because, for example, of issues such as confounding).[11] Causal inference techniques used with experimental data require additional assumptions to produce reasonable inferences with observation data.[12] The difficulty of causal inference under such circumstances is often summed up as "correlation does not imply causation". OverviewECA postulates that there exist data analysis procedures performed on specific subsets of variables within a larger set whose outputs might be indicative of causality between those variables.[1] For example, if we assume every relevant covariate in the data is observed, then propensity score matching can be used to find the causal effect between two observational variables.[2] Granger causality can also be used to find the causality between two observational variables under different, but similarly strict, assumptions.[13] The two broad approaches to developing such procedures are using operational definitions of causality[3] or verification by "truth" (i.e., explicitly ignoring the problem of defining causality and showing that a given algorithm implies a causal relationship in scenarios when causal relationships are known to exist, e.g., using synthetic data[1]). Operational Definitions of CausalityClive Granger created the first operational definition of causality in 1969.[14] Granger made the definition of probabilistic causality proposed by Norbert Wiener operational as a comparison of variances.[15]Some authors prefer using ECA techniques developed using operational definitions of causality because they believe it may help in the search for causal mechanisms.[3][16] Verification by "Truth"Peter Spirtes, Clark Glymour, and Richard Scheines introduced the idea of explicitly not providing a definition of causality.[1] Spirtes and Glymour introduced the PC algorithm for causal discovery in 1990.[17] Many recent causal discovery algorithms follow the Spirtes-Glymour approach to verification.[18]TechniquesThere are many surveys of causal discovery techniques.[1][3][18][19][20][21] This section lists the well-known techniques. Bivariate (or "pairwise")
Multivariate
Many of these techniques are discussed in the tutorials provided by the Center for Causal Discovery (CCD) [https://www.ccd.pitt.edu/video-tutorials/]. Use-case ExamplesSocial ScienceThe PC algorithm has been applied to several different social science data sets.[1] MedicineThe PC algorithm has been applied to medical data.[26] Granger causality has been applied to fMRI data.[27] CCD tested their tools using biomedical data [https://www.ccd.pitt.edu/biomedical-science/]. PhysicsECA is used in physics to understand the physical causal mechanisms of the system, e.g., in geophysics using the PC-stable algorithm (a variant of the original PC algorithm)[28] and in dynamical systems using pairwise asymmetric inference (a variant of convergent cross mapping).[29] CriticismThere is debate over whether or not the relationships between data found using causal discovery are actually causal.[1][23] Judea Pearl has emphasized that causal inference requires a causal model developed by "intelligence" through an iterative process of testing assumptions and fitting data.[5] Response to the criticism points out that assumptions used for developing ECA techniques may not hold for a given data set[1][12][30][31][32] and that any causal relationships discovered during ECA are contingent on these assumptions holding true[23][33] Software PackagesComprehensive toolkits
Tetrad is an open source GUI-based Java program that provides a collection of causal discovery algorithms . The algorithm library used by Tetrad is also available as a command-line tool, Python API, and R wrapper [https://bd2kccd.github.io/docs/causal-cmd/].
JIDT is an open source Java library for performing information-theoretic causal discovery (i.e., transfer entropy, conditional transfer entropy, etc.)[https://github.com/jlizier/jidt/wiki/Documentation]. Examples of using the library in MATLAB, GNU Octave, Python, R, Julia and Clojure are provided in the documentation [https://github.com/jlizier/jidt].
pcalg is an R package that provides some of the same causal discovery algorithms provided in Tetrad [https://cran.r-project.org/web/packages/pcalg/vignettes/pcalgDoc.pdf]. Specific TechniquesGranger causality
convergent cross mapping
LiNGAM
There is also a collection of tools and data maintained by the Causality Workbench team and the CCD team [https://www.ccd.pitt.edu/tools/]. References1. ^1 2 3 4 5 6 7 8 9 10 {{cite book |last=Spirtes, P.; Glymour, C.; Scheines, R.|year = 2012|title = Causation, Prediction, and Search| publisher = Springer Science & Business Media| isbn = 978-1461227489}} {{Authority control}}{{DEFAULTSORT:Exploratory Causal Analysis}}2. ^1 2 {{cite book |last=Rosenbaum| first = Paul|year = 2017|title = Observation and Experiment: An Introduction to Causal Inference| publisher = Harvard University Press| isbn = 9780674975576}} 3. ^1 2 3 {{cite book |last=McCracken| first = James|year = 2016|title = Exploratory Causal Analysis with Time Series Data (Synthesis Lectures on Data Mining and Knowledge Discovery)| publisher = Morgan & Claypool Publishers| isbn = 978-1627059343}} 4. ^{{cite book| last = Tukey| first = John W.| year = 1977| title = Exploratory Data Analysis | publisher = Pearson| isbn = 978-0201076165| title-link = Exploratory Data Analysis}} 5. ^1 {{cite book |last=Pearl| first = Judea|year = 2018|title = The Book of Why: The New Science of Cause and Effect | publisher = Basic Books| isbn = 978-0465097616}} 6. ^{{cite book |last=Kleinberg| first = Samantha|year = 2015|title = Why: A Guide to Finding and Using Causes| publisher = O'Reilly Media, Inc.| isbn = 978-1491952191}} 7. ^{{cite book |last=Illari |first=P. |last2=Russo |first2=F.|year=2014|title=Causality: Philosophical Theory meets Scientific Practice|publisher=OUP Oxford|isbn=978-0191639685}} 8. ^{{cite book | last=Fisher|first=R.|year=1937|title=The design of experiments|publisher=Oliver And Boyd}} 9. ^{{cite book | last=Hill|first=B.|year=1955|title=Principles of Medical Statistics|publisher=Lancet Limited}} 10. ^{{cite book|last=Halpern|first=J.|year=2016|title=Actual Causality|publisher=MIT Press|isbn=978-0262035026}} 11. ^{{cite book|last=Pearl |first=J. |last2=Glymour |first2=M. |last3=Jewell |first3=N. 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