Causality

Causality (also referred to as causation) is the agency or efficacy that connects one process (the cause) with another (the effect), where the first is understood to be partly responsible for the second, and the second is dependent on the first. In general, a process has many causes, which are said to be causal factors for it, and all lie in its past. An effect can in turn be a cause of many other effects, which all lie in its future.

Causality is an abstraction that indicates how the world progresses, so basic a concept that it is more apt as an explanation of other concepts of progression than as something to be explained by others more basic. The concept is like those of agency and efficacy. For this reason, a leap of intuition may be needed to grasp it. Accordingly, causality is built into the conceptual structure of ordinary language.

The main we have to know for science application is:

Correlation is NOT causation.

There are some methods to infer causal effects in time-series as Transfer information or Granger causality test.

See also

Material

• Causal modeling

Papers

• Pearl, J. (1998). Graphs, causality, and structural equation models. Sociological Methods & Research, 27(2), 226-284.
• Pearl, J. (2003). Causality: models, reasoning and inference. Econometric Theory, 19, 675-685.
• Greenland, S.; Brumback, B. (2002). An overview of relations among causal modelling methods. International Journal of Epidemiology 31 (5): 1030-1037

Books

• Judea Pearl (2000). Causality: Models, Reasoning, and Inference. Cambridge University Press.
• Berzuini, C., Dawid, P., & Bernardinell, L. (2012). Causality: Statistical perspectives and applications. John Wiley & Sons.
• Bunge, M. A. (1959). Causality and Modern Science. Dover Publications. Interesant book of philosophy.