Learning Representations Using Causal Invariance

In **EGC 2020**, vol. RNTI-E-36, pp.5-6

Abstract

Learning algorithms often capture spurious correlations present in the training data distribution instead of addressing the task of interest. Such spurious correlations occur because the

data collection process is subject to uncontrolled confounding biases. Suppose however that

we have access to multiple datasets exemplifying the same concept but whose distributions

exhibit different biases. Can we learn something that is common across all these distributions,

while ignoring the spurious ways in which they differ? This can be achieved by projecting the

data into a representation space that satisfy a causal invariance criterion. This idea differs in

important ways from previous work on statistical robustness or adversarial objectives. Similar

to recent work on invariant feature selection, this is about discovering the actual mechanism

underlying the data instead of modeling its superficial statistics.

data collection process is subject to uncontrolled confounding biases. Suppose however that

we have access to multiple datasets exemplifying the same concept but whose distributions

exhibit different biases. Can we learn something that is common across all these distributions,

while ignoring the spurious ways in which they differ? This can be achieved by projecting the

data into a representation space that satisfy a causal invariance criterion. This idea differs in

important ways from previous work on statistical robustness or adversarial objectives. Similar

to recent work on invariant feature selection, this is about discovering the actual mechanism

underlying the data instead of modeling its superficial statistics.