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i get the sense that back in the 90s, statisticians must have been really rude and dismissive to Judea Pearl, and he might not have gotten over it. it seems ve

i get the sense that back in the 90s, statisticians must have been really rude and dismissive to Judea Pearl, and he might not have gotten over it. it seems very plausible to me that at the time, many people outside CS would have considered a graph to be a type of illustration not a "legitimate" mathematical structure, and wouldn't have understood or cared about computational complexity arguments about performing inference or computing independence. it does seem to be the case that for various reasons, for economics and population-level social science, there's limited advantage to using DAGs. I have also noticed as a causal inference outsider in machine learning land, people use DAGs or SWIGs to write down assumptions, and then translate to potential outcomes to derive estimators. And for people who do automated causal discovery, I haven't heard of anyone trying to use a potential-outcomes-based representation for their system. so i buy Pearl's argument that DAGs are the "right" data structure for representing causality, both for humans and computers. But many social scientists may not have any reason to care about this, because automated causal discovery is hopeless for their applications, and for methodological reasons any reasonable model has to be such that representing things directly as potential outcomes is manageable anyway.

TeMPOraL 6y ago
> it does seem to be the case that for various reasons, for economics and population-level social science, there's limited advantage to using DAGs. A tangent: on the one hand, I do think DAGs are the best thing since sliced bread, and I wish more people would embrace them and add them to their toolbox as a fundamental modelling tool. On the other hand, the next step is realization that DAGs aren't sufficient. Lack of cycles is nice for analysis and implementation, but it's also a drawback. The world is running on feedback loops, and yet this seems to be a secret restricted only to specialists deep inside their respective fields. I'd wish the public was more accustomed to working with dynamic models. (I don't think cyclic graphs help much with causality analysis, as presumably cycles would imply time travel.)
- currymj 6y ago
  yeah, i've wondered about this too. the natural thing to do is to just unroll the models in time. the classic case of this is a hidden Markov Model, but you can easily have a more complicated time-indexed DAG. There are also some interesting connections to reinforcement learning (MDP can be expressed as DAG e.g. https://www.microsoft.com/en-us/research/wp-content/uploads/2013/11/bottou13a.pdf https://www.microsoft.com/en-us/research/wp-content/uploads/..., and then many RL algorithms are equivalent to causal inference estimators). it seems like people deal with equilibria and feedback loops in a pretty ad-hoc way though.

TeMPOraL 6y ago

> it does seem to be the case that for various reasons, for economics and population-level social science, there's limited advantage to using DAGs. A tangent: on the one hand, I do think DAGs are the best thing since sliced bread, and I wish more people would embrace them and add them to their toolbox as a fundamental modelling tool. On the other hand, the next step is realization that DAGs aren't sufficient. Lack of cycles is nice for analysis and implementation, but it's also a drawback. The world is running on feedback loops, and yet this seems to be a secret restricted only to specialists deep inside their respective fields. I'd wish the public was more accustomed to working with dynamic models. (I don't think cyclic graphs help much with causality analysis, as presumably cycles would imply time travel.)

currymj 6y ago
yeah, i've wondered about this too. the natural thing to do is to just unroll the models in time. the classic case of this is a hidden Markov Model, but you can easily have a more complicated time-indexed DAG. There are also some interesting connections to reinforcement learning (MDP can be expressed as DAG e.g. https://www.microsoft.com/en-us/research/wp-content/uploads/2013/11/bottou13a.pdf https://www.microsoft.com/en-us/research/wp-content/uploads/..., and then many RL algorithms are equivalent to causal inference estimators). it seems like people deal with equilibria and feedback loops in a pretty ad-hoc way though.