by Phillip W. Magness
The American Institute for Economic Research
The problem of causal inference presents one of the great challenges of empirical analysis. While it is relatively easy to find patterns in data that appear to move over time in response to overlapping events, it is much harder to show that those events specifically caused the data to move as expected.
Think about how presidents often cite positive economic data such as GDP growth or the stock market as vindication of their own economic policies. Prior to early March 2020 this was a favorite tweeting topic of Donald Trump, although his predecessors almost all made similar claims. While this argument makes for a useful campaign pitch, it is poor social science and would never pass empirical scrutiny.