As researchers pursued the inevitable AGI in machines, there has been a renewed interest in the idea of causality in models. There are significant implications to applying machine learning to problems of causal inference in fields such as healthcare, economics and education.
Here are a few top works that acknowledge the challenges and offer solutions to the causal inference in machines:
- The Seven Tools Of Causal Inference
- A Causal Bayesian Networks Viewpoint on Fairness
- Causal Inference And The Data-fusion Problem
- Reinforcement Knowledge Graph Reasoning for Explainable Recommendation
- Double/Debiased Machine Learning for Treatment and Causal Parameters
- Causal Regularization
- Unbiased Scene Graph Generation
In the paper “The Seven Tools Of Causal Inference”, Judea Pearl who has championed the notion of causal inference in machines, argues that causal reasoning is an indispensable component of human thought that should be formalized and algorithimitized towards achieving human-level machine intelligence. Pearl, in this paper, analyses some of the challenges in the form of a three-level hierarchy, and shows that inference to different levels requires a causal model of one’s environment. He has also described seven cognitive tasks that require tools from those two levels of inference.
The original article can be found here.
In the field of causal reasoning, Professor Judea Pearl is a pioneer for developing a theory of causal and counterfactual inference based on structural models. In 2020, Professor Pearl is also awarded as World Leader in AI World Society (AIWS.net) by Michael Dukakis Institute for Leadership and Innovation (MDI) and Boston Global Forum (BGF). In the future, Professor Judea will also contribute to Causal Inference for AI transparency, which is one of important AIWS topics on AI Ethics.