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Is there nothing between RCTs and "high-fives" to estimate causal effects? Economists seem to run awfully long journal articles if they all boil down to high fi

Is there nothing between RCTs and "high-fives" to estimate causal effects? Economists seem to run awfully long journal articles if they all boil down to high fives.

sbierwagen 7y ago
There are many philosophy journals filled with long articles that also have no causal connection with reality. Why would you assume any given academic journal has to be publishing things that make sense, or are useful?
- QuesnayJr 7y ago
  Why would you not? I don't even like philosophy, and I wouldn't assume they're just publishing nonsense.
  - dwoozle 7y ago
    Because of a Transformative Hermeneutics of Quantum Gravity.
    - nemo 7y ago
      Worth noting that Transformative Hermeneutics of Quantum Gravity was not publishing in a philosophy journal. It was published in a litcrit journal which is a very distinct discipline.
bjourne 7y ago
Piketty's Capital in the 21st century would be an example of something in between. His main hypothesis is that unless there is some intervention wealth accumulates until almost all of it is concentrated among just a few. He uses lots and lots of statistics to support his theory. He tells us that it happens but is unable to tell us exactly why.
- kortilla 7y ago
  Is that really any better though. Unless those statistics are shown to have a predictive effect on money flow, he’s just publishing something to fit a model and metaphorical high fives are thrown around by people that already agree with the hypothesis.
benibela 7y ago
There is a lot of research on how to estimate causal effects from data. And text books. "Causality" by Pearl about causal models in general. "Causation, Prediction, and Search" by Spirtes about how to learn the models from data. For example assume the world consists of three random variables A, B, and C. If A causes B and B causes C (as DAG A -> B -> C), then A and C are correlated. But if the model is A -> B <- C, then A and C are not correlated. But conditioned on B, A and C are correlated in A->B<-C and not correlated in A->B->C. So you can falsify such causal models without an rct

Is there nothing between RCTs and "high-fives" to estimate causal effects? Economists seem to run awfully long journal articles if they all boil down to high fives.

sbierwagen 7y ago

There are many philosophy journals filled with long articles that also have no causal connection with reality. Why would you assume any given academic journal has to be publishing things that make sense, or are useful?

QuesnayJr 7y ago
Why would you not? I don't even like philosophy, and I wouldn't assume they're just publishing nonsense.
- dwoozle 7y ago
  Because of a Transformative Hermeneutics of Quantum Gravity.
  - nemo 7y ago
    Worth noting that Transformative Hermeneutics of Quantum Gravity was not publishing in a philosophy journal. It was published in a litcrit journal which is a very distinct discipline.

bjourne 7y ago

Piketty's Capital in the 21st century would be an example of something in between. His main hypothesis is that unless there is some intervention wealth accumulates until almost all of it is concentrated among just a few. He uses lots and lots of statistics to support his theory. He tells us that it happens but is unable to tell us exactly why.

kortilla 7y ago
Is that really any better though. Unless those statistics are shown to have a predictive effect on money flow, he’s just publishing something to fit a model and metaphorical high fives are thrown around by people that already agree with the hypothesis.

benibela 7y ago

There is a lot of research on how to estimate causal effects from data. And text books. "Causality" by Pearl about causal models in general. "Causation, Prediction, and Search" by Spirtes about how to learn the models from data. For example assume the world consists of three random variables A, B, and C. If A causes B and B causes C (as DAG A -> B -> C), then A and C are correlated. But if the model is A -> B <- C, then A and C are not correlated. But conditioned on B, A and C are correlated in A->B<-C and not correlated in A->B->C. So you can falsify such causal models without an rct