A Primer on Causal Analysis
arXiv:1806.01488v17 citations
Originality Synthesis-oriented
AI Analysis
This provides a conceptual framework for researchers and practitioners in statistics and machine learning, but it is incremental as it maps existing approaches.
The paper tackles the problem of causal effect estimation from observational data, particularly for discrete random variables, and introduces four schools of thought for causal analysis.
We provide a conceptual map to navigate causal analysis problems. Focusing on the case of discrete random variables, we consider the case of causal effect estimation from observational data. The presented approaches apply also to continuous variables, but the issue of estimation becomes more complex. We then introduce the four schools of thought for causal analysis