In social science we are sometimes in the position of studying descriptive questions (In what places do working-class whites vote for Republicans? In what eras has social mobility been higher in the United States than in Europe? In what social settings are different sorts of people more likely to act strategically?). Answering descriptive questions is not easy and involves issues of data collection, data analysis, and measurement (how one should define concepts such as “working-class whites,” “social mobility,” and “strategic”) but is uncontroversial from a statistical standpoint. All becomes more difficult when we shift our focus from what to what if and why. Consider two broad classes of inferential questions:
1. Forward causal inference. What might happen if we do X? What are the effects of smoking on health, the effects of schooling on knowledge, the effect of campaigns on election outcomes, and so forth?
2. Reverse causal inference. What causes Y? Why do more attractive people earn more money? Why do many poor people vote for Republicans and rich people vote for Democrats? Why did the economy collapse?
Causality and Statistical Learning