Causal Discovery vs Causal Inference Using Python

Causal discovery and causal inference are two different approaches used to analyze the relationships between variables. Causal discovery aims to uncover unknown causal structures from data using algorithms like the PC algorithm or LiNGAM, which infer relationships based on conditional dependencies. The outcome of causal discovery is typically a causal graph, where directed edges represent causal relationships. On the other hand, causal inference involves estimating the effect of a hypothesized causal structure, where the relationships between variables are already assumed, such as estimating the impact of a marketing campaign on sales. This approach uses methods like linear regression or propensity score matching to estimate the magnitude of these effects, assuming the structure is known in advance. In causal discovery, no prior knowledge is required about which variables cause others, while in causal inference, the causal structure is explicitly defined. In a practical example, using a dataset with 'Campaign', 'AdvertisingSpend', and 'Sales', causal discovery can reveal the direction of relationships, such as advertising spend influencing both campaign decisions and sales. Causal inference, however, estimates the effect of campaigns on sales, controlling for advertising spend. Both methods provide valuable insights for decision-making but differ in their assumptions and methods for identifying and estimating causal effects. These techniques are widely used in fields like marketing, product development, and policy evaluation.