> The method, in the form of an algorithm, takes in data that have been collected over time, such as the changing populations of different species in a marine environment. From those data, the method measures the interactions between every variable in a system and estimates the degree to which a change in one variable (say, the number of sardines in a region over time) can predict the state of another (such as the population of anchovy in the same region).
I also read the introduction of the paper. Maybe I misunderstood something about causal inference, but I thought from data alone one could only infer correlations or associations (in general). To talk about "causal" links, I thought you need either to assume a particular model of the data generation process, or perform some interventions on the system to be able to decide the direction of the arrows in the "links" in general.
I'm not saying that the paper is wrong or anything, it looks super useful! It's just that one should be careful when writing/reading the word "causal".
Correlation also assumes model of the data generating process, but you are correct in thinking that talking about causal links imposes even stronger assumption on the model and data structure for making inference. And then further you have to take a very narrow and convenient interpretation of what causality means (e.g. can't be at the actual level of individual samples, can't manifest through cycles or loops in the variables etc), which is even more of a vexing philosophical question than even the thorny questions in classical statistical inference
You can always restrict the meaning of "causality".
E.g. Granger casusality means that A is typically detected before B and not the other way around (so not mere correlation). It's a moby useful concept.
"collected over time" is the operational phrase. You should be able to determine causality, at least partially, if you know the change in correlated variables occur at different times.
> The method, in the form of an algorithm, takes in data that have been collected over time, such as the changing populations of different species in a marine environment. From those data, the method measures the interactions between every variable in a system and estimates the degree to which a change in one variable (say, the number of sardines in a region over time) can predict the state of another (such as the population of anchovy in the same region).
I also read the introduction of the paper. Maybe I misunderstood something about causal inference, but I thought from data alone one could only infer correlations or associations (in general). To talk about "causal" links, I thought you need either to assume a particular model of the data generation process, or perform some interventions on the system to be able to decide the direction of the arrows in the "links" in general.
I'm not saying that the paper is wrong or anything, it looks super useful! It's just that one should be careful when writing/reading the word "causal".