Causality and machine learning

Abstract

Despite the recent success and widespread applications of machine learning (ML) algorithms for classification and prediction in a variety of fields, they face difficulty in interpretability, trustworthiness and generalization. One of the main reasons for this is that these algorithms are building black-box models by learning statistical associations between the given ‘input’ and its ‘output’. Decisions made solely based on ‘associational learning’ are insufficient to provide explanations and hence difficult to be employed in real world tasks requiring transparency and reliability. To overcome these limitations of ML algorithms, researchers are moving towards ‘causal machine learning’ by aiding ML decision-making based on causal reasoning and understanding. We will discuss ‘the science of causality’, its requirements in ML and possible means of integration with ML. We will also compare different ML algorithms based on their performance in learning temporal order/ structure in single time series as well as their ability to classify coupled pairs of time-series based on their cause-effect (or driver-driven) relationship.