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You flip a coin to randomize choice of a treatment and record the results. The coin-flips+results is a stream of binary data that can then be compressed well or

by gwern 3y ago

You flip a coin to randomize choice of a treatment and record the results. The coin-flips+results is a stream of binary data that can then be compressed well or poorly. A compressor which has built a correct causal model of the effects, whatever those are, will compress better than one which is unable to and can only blindly predict pretreatment results (or worse, predict conditional on the correlations which were just broken by the coin-flip, thereby actually wasting bits to fix its especially erroneous predictions). This is in line with the compression paradigm. Where do you disagree? Do you think that causal models are completely useless for shortening predictions? Or do you think causality just doesn't exist? > "Compressing tables of data" is something they do in the pseudosciences -- as you've seen, none of it is reproducible: "IQ" is just a compression of survey quizzes. Do you really think it exists? That's not even close to correct about IQ. You can measure it from lots of things which are not 'survey quizzes'; fMRIs, for example.

mjburgess 3y ago
fMRIs are also pseudoscience. Again, just associative models of blood flow. Causal models do not compress measurement data. There are an infinite number of ways of measuring any phenomena (consider, all possible devices which measure temperature) -- in this sense, they non-uniquely compress "all possible data about the phenomena across all possible measurement systems". Ie., even with "all possible data" there are an infinite number of lossess models of it. (But we would not even want a lossless model, since "all possible data" includes all measurement systems which have their own dynamics). When we have an explanatory model of heat (as the kinetics of molecules), we have a textbook of explanations which we use (via reasoning, imagination, etc.) to write down whole families of causal models. So when creating a new device we can determine what it's behaviour will be. This has nothing to do with a compression of measurement data. We do not, nor ever have, nor even cloud, determine the causal structure of reality using compression of measurements. Measurement devices are physical systems whose properties are causally determined by target devices -- how they are determined is not "in the data". Absent knowing this, ie., science, the data is just a description of the measurement system -- not the target.

by gwern 3y ago

mjburgess 3y ago

fMRIs are also pseudoscience. Again, just associative models of blood flow. Causal models do not compress measurement data. There are an infinite number of ways of measuring any phenomena (consider, all possible devices which measure temperature) -- in this sense, they non-uniquely compress "all possible data about the phenomena across all possible measurement systems". Ie., even with "all possible data" there are an infinite number of lossess models of it. (But we would not even want a lossless model, since "all possible data" includes all measurement systems which have their own dynamics). When we have an explanatory model of heat (as the kinetics of molecules), we have a textbook of explanations which we use (via reasoning, imagination, etc.) to write down whole families of causal models. So when creating a new device we can determine what it's behaviour will be. This has nothing to do with a compression of measurement data. We do not, nor ever have, nor even cloud, determine the causal structure of reality using compression of measurements. Measurement devices are physical systems whose properties are causally determined by target devices -- how they are determined is not "in the data". Absent knowing this, ie., science, the data is just a description of the measurement system -- not the target.