How to Find Productive Causes in Big Data: An Information Transmission Account

  • Billy Wheeler Department of Philosophy (Zhuhai), Sun Yat-Sen University
Słowa kluczowe: causation, big data, data-intensive science, machine learning, conserved quantities, causal processes


It has been argued that the use of big data in scientific research obviates the need for causal knowledge in making sound predictions and interventions. Whilst few accept that this claim is true, there is an ongoing discussion about what effect, if any, big data has on scientific methodology and, in particular, the search for causes. One response has been to show that the automated analysis of big data by a computer program can be used to find causes in addition to mere correlations. However, up until now it has only been demonstrated how this can be achieved with respect to difference-making causes. Yet it is widely acknowledged that scientists need evidence of both “difference-making” and “production” in order to infer a genuine causal link. This paper fills in the gap by outlining how computer-assisted discovery in big data can find productive causes. This is achieved by developing an inference rule based on a little-known causal process theory called the information transmission account.


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Wheeler, B. (2018). How to Find Productive Causes in Big Data: An Information Transmission Account. Filozofia Nauki, 26(4), 5-28.