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Preliminaries for CRL

In the previous blog post we discussed and motivated the need for a causal approach to reinforcement learning. We argued that reinforcement learning naturally falls on the interventional rung of...

Causal Reinforcement Learning: A Primer

As part of any honours degree at the University of Cape Town, one is obliged to write a thesis droning on about some topic. Luckily for me, applied mathematics can...

Faithfulness

In the last dicussion we sought to rigorously define counterfactual statements and distributions in terms of our DAG formalism of causal inference. This appeared fruitful but the theory is certainly...

Reaching Rung 3: Counterfactual Reasoning

In our last discussion we discussed the so-called ‘rung two’ of the ladder of causation, discussing interventions and randomisation in control trials. This is an incredibly important field in the...

Interventions and Multivariate SCMs

Last time we discussed how we can learn causal structure from data and thought about how this relates to machine learning. Specifically, we noticed that having more data in a...

Causality and Machine Learning

Last time we briefly discussed the theory needed to start thinking about how we can learn, in the statistical sense, causal information from ‘dumb’ data. Some key points were that...

Learning Causal Models

In the last episode we developed the first tools we need to develop the theory needed to formalise interventions and counterfactual reasoning. In this article we’ll discuss how we can...

Causal Models

Last time we discussed and motivated the need for a modern theory of causal inference. We developed some of the basic principles necessary to develop this theory, but we have...

Uncertainty in Model Based RL

Perhaps we could use uncertainty estimation to detect where the model may be wrong and then correct for these potential errors without having to collect much more data. This uncertainty...