For the past few decades, empirical research has shunned all talk of causation. Scholars use their causal intuitions but they only ever talk about correlation. Smoking is “associated to” cancer, being overweight is “correlated with” higher morbidity rates, college education is the strongest “correlate of” Trump’s vote gains over Romney, and so on and so forth. Empirical researchers don't like to use causal language because they think that causal concepts are not well-defined. It is a hegemonic postulate of modern statistics and econometrics that all falsifiable claims can be stated in the language of modern probability. Any talk of causation is frowned upon because causal claims simply cannot be cast in the language of probability. For instance, there is no way to state in the language of probability that smoking causes cancer, that the tides are caused by the moon or that rain causes the lawn to get wet.
Causal Inference from Linear Models
Causal Inference from Linear Models
Causal Inference from Linear Models
For the past few decades, empirical research has shunned all talk of causation. Scholars use their causal intuitions but they only ever talk about correlation. Smoking is “associated to” cancer, being overweight is “correlated with” higher morbidity rates, college education is the strongest “correlate of” Trump’s vote gains over Romney, and so on and so forth. Empirical researchers don't like to use causal language because they think that causal concepts are not well-defined. It is a hegemonic postulate of modern statistics and econometrics that all falsifiable claims can be stated in the language of modern probability. Any talk of causation is frowned upon because causal claims simply cannot be cast in the language of probability. For instance, there is no way to state in the language of probability that smoking causes cancer, that the tides are caused by the moon or that rain causes the lawn to get wet.