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> to determine causality, we typically have to perturb the system Couldn't agree more! For computer models to understand causality, it must be able to interact

> to determine causality, we typically have to perturb the system Couldn't agree more! For computer models to understand causality, it must be able to interact with the environment and probe it. I think understanding causality is one and the same as reinforcement learning, where a computer model learns to interact with its environment

jbay808 6y ago
But many datasets will be full of recorded perturbations already. Can't you usually hunt for the evidence you need in the data you have? The Michelson-Morley experiment was enough data to get special relativity, for example.
- goatlover 6y ago
  Einstein was inspired to come up with special relativity from that experiment because he had the relevant concepts to draw upon to think through various thought experiments. That and the math to back up his intuition.
  - jostmey 6y ago
    Yes, but Einstein was able to learn cause and effect by interacting with the physical world as a child. Without being able to interact with an environment, the I believe the ability to learn causality is limited if not impossible
6gvONxR4sf7o 6y ago
Alternatively, we need to extend our typical notion of a dataset to include where it came from/how it was perturbed. If you give your best statistician a dataset without column names and a description of the experiment, they aren't going to be able to do causal inference. We need to make those things more available to the machines too, in a structured way.
zwaps 6y ago
The majority of the books listed in the first link in these comments are about whether and when we can determine causality given observational data - data where the system is perturbed, just not by us (and not as we would ideally like).
- a9h74j 6y ago
  I have not looked at the links, but aren't there people who use Bode plots to estimate direction-of-causality, given time-series data? IIRC there are basic relationships between wide-frequency phase behavior and, e.g., impulse-response functions. There is apparent phase information beyond correlation and ANOVA.

jbay808 6y ago

But many datasets will be full of recorded perturbations already. Can't you usually hunt for the evidence you need in the data you have? The Michelson-Morley experiment was enough data to get special relativity, for example.

goatlover 6y ago
Einstein was inspired to come up with special relativity from that experiment because he had the relevant concepts to draw upon to think through various thought experiments. That and the math to back up his intuition.
- jostmey 6y ago
  Yes, but Einstein was able to learn cause and effect by interacting with the physical world as a child. Without being able to interact with an environment, the I believe the ability to learn causality is limited if not impossible

6gvONxR4sf7o 6y ago

Alternatively, we need to extend our typical notion of a dataset to include where it came from/how it was perturbed. If you give your best statistician a dataset without column names and a description of the experiment, they aren't going to be able to do causal inference. We need to make those things more available to the machines too, in a structured way.

zwaps 6y ago

The majority of the books listed in the first link in these comments are about whether and when we can determine causality given observational data - data where the system is perturbed, just not by us (and not as we would ideally like).

a9h74j 6y ago
I have not looked at the links, but aren't there people who use Bode plots to estimate direction-of-causality, given time-series data? IIRC there are basic relationships between wide-frequency phase behavior and, e.g., impulse-response functions. There is apparent phase information beyond correlation and ANOVA.