Causal Inference from Big Data?
Serena Galli · dialectica · 2023
In his book Big Data [-@pietsch_w:2021], Wolfgang Pietsch defends the view that variational induction, which stands in the tradition of Mill’s methods, allows us to derive conclusions about causal relationships from observational data and that the algorithms that are most successfully applied for big data analysis implement variational induction. In his opinion, the fact that the analysis of big data by machine learning algorithms enables reliable predictions and effective interventions in the world supports the assumption that these algorithms correctly identify causal relationships. In the first part of the paper, I argue that attempts to infer causal relationships from observational data by variational induction face fundamental difficulties. Furthermore, I contend that these difficulties are not due to the specific way in which the method is spelled out but are manifestations of a general underdetermination problem. In the second part, I consider Pietsch’s claim that the practical benefit of big data approaches indicates that variational induction implemented by machine learning algorithms generates causal knowledge. I provide a critical assessment of his notion of causal knowledge, and I argue that his conclusion relies on an inaccurate depiction of scientific practice.