Revealing causal links in complex systems
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Getting to the heart of causality is central to understanding the world around us. What causes one variable — be it a biological species, a voting region, a company stock, or a local climate — to shift from one state to another can inform how we might shape that variable in the future.
But tracing an effect to its root cause can quickly become intractable in real-world systems, where many variables can converge, confound, and cloud over any causal links.
Now, a team of MIT engineers hopes to provide some clarity in the pursuit of causality. They developed a method that can be applied to a wide range of situations to identify those variables that likely influence other variables in a complex system.
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).
