Almost every statistics instructor emphasizes to their classes that a strong association does not necessarily imply causation. How researchers could draw causal interpretations from the results of their studies remains a challenging yet interesting question. First, the question concerning how potential causal factors can be identified prior to modeling is even more important than making causal interpretations after the analysis. This book aims to evaluate the claim that automated data mining constitutes a paradigm shift in causal discovery. The data mining approach overlooks how conceptualization affects projection and causal inferences. On the contrary, the abductive approach recognizes the role of conceptualization in every step of inquiry. A thorough inquiry with respect to causal inferences should include abduction, deduction, and induction. This non-technical book is written for researchers who are interested in causal inferences and have basic knowledge of philosophy and quantitative methods.

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