Data analysis is primarily concerned with causal questions. For example, did the fertilizer cause the crops to grow? Or, can a given sickness be prevented? Or, why is my friend depressed? 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. Causal inference techniques used with experimental data require additional assumptions to produce reasonable inferences with observation data. The difficulty of causal inference under such circumstances is often summed up as "correlation does not imply causation".
Overview
ECA postulates that there existdata analysis procedures performed on specific subsets of variables within a larger set whose outputs might be indicative of causality between those variables. 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. Granger causality can also be used to find the causality between two observational variables under different, but similarly strict, assumptions. The two broad approaches to developing such procedures are using operational definitions of causality or verification by "truth".
created the first operational definition of causality in 1969. Granger made the definition of probabilistic causality proposed by Norbert Wiener operational as a comparison of variances. Some authors prefer using ECA techniques developed using operational definitions of causality because they believe it may help in the search for causal mechanisms.
Verification by "truth"
, Clark Glymour, and Richard Scheines introduced the idea of explicitly not providing a definition of causality. Spirtes and Glymour introduced the PC algorithm for causal discovery in 1990. Many recent causal discovery algorithms follow the Spirtes-Glymour approach to verification.
Techniques
There are many surveys of causal discovery techniques. This section lists the well-known techniques.
Many of these techniques are discussed in the tutorials provided by the Center for Causal Discovery .
Use-case examples
Social science
The PC algorithm has been applied to several different social science data sets.
Medicine
The PC algorithm has been applied to medical data. Granger causality has been applied to fMRI data. CCD tested their tools using biomedical data .
Physics
ECA is used in physics to understand the physical causal mechanisms of the system, e.g., in geophysics using the PC-stable algorithm and in dynamical systems using pairwise asymmetric inference.
Criticism
There is debate over whether or not the relationships between data found using causal discovery are actually causal. Judea Pearl has emphasized that causal inference requires a causal model developed by "intelligence" through an iterative process of testing assumptions and fitting data. Response to the criticism points out that assumptions used for developing ECA techniques may not hold for a given data set and that any causal relationships discovered during ECA are contingent on these assumptions holding true
