Interpretable causal systems: interpretability and causality in machine learning for human and nonhuman decision-making

In order to integrate machine learning into human decision-making in a useful way, we must trust machine learning systems enough in our reasoning processes. To evaluate a system’s trustworthiness, humans naturally seek interpretable causal systems to understand outcomes, make decisions, and integrate feedback. This thesis presents four explorations into interpretable causal systems, progressing from associational interpretability up the “Ladder of Causation” to counterfactual representation. In the first contribution, I introduce a Bayesian nonparametric method for calculating the expected value and volatility of gradients in the data; this helps inform further experiments for deriving causal effects and interpreting changes when faced with decision-making scenarios. In the second, I show how latent expert knowledge can be collected to produce an interpretable model with policy-relevant consequences. In the third, I interpret time series treatment effect inference as a problem naturally handled by multitask Gaussian processes, addressing several shortcomings in a popular approach. In the fourth, I consider twin networks, a novel but understudied method of representing counterfactual questions; I show that one can sometimes improve on computational cost, error, and insight by representing counterfactual problems as twin networks. I then introduce a prototype probabilistic programming engine that natively leverages the efficiencies gained from twin networks. I close by considering the future of how interpretability and causality might integrate into machine learning-driven decision-making and speculate that causality is a key component in achieving efficient, generalisable intelligence in a variety of applications.