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Interventional Causal Representation Learning
Kartik Ahuja · Yixin Wang · Divyat Mahajan · Yoshua Bengio

Fri Dec 09 08:40 AM -- 08:50 AM (PST) @

The theory of identifiable representation learning aims to build general-purpose methods that extract high-level latent (causal) factors from low-level sensory data. Most existing works focus on identifiable representation learning with observational data, relying on distributional assumptions on latent (causal) factors. However, in practice, we often also have access to interventional data for representation learning, e.g. from robotic manipulation experiments in robotics, from genetic perturbation experiments in genomics, or from electrical stimulation experiments in neuroscience. How can we leverage interventional data to help identify high-level latents? To this end, we explore the role of interventional data for identifiable representation learning in this work. We study the identifiability of latent causal factors with and without interventional data, under minimal distributional assumptions on latents. We prove that, if the true latent maps to the observed high-dimensional data via a polynomial function, then representation learning via minimizing standard reconstruction loss (used in autoencoders) can identify the true latents up to affine transformation. If we further have access to interventional data generated by hard $do$ interventions on some latents, then we can identify these intervened latents up to permutation, shift and scaling.

#### Author Information

##### Divyat Mahajan (Microsoft Research)

I am a Research Fellow at Microsoft Research Lab India, where I work with Amit Sharma on Machine Learning and Causal Inference. Prior to joining MSR, I completed my undergraduate double major program in Mathematics and Computer Science from the Indian Institute of Technology, Kanpur. Interests: Machine Learning | Causal Inference | Explainability, Generalization and Robustness in Deep Learning

##### Yoshua Bengio (Mila / U. Montreal)

Yoshua Bengio is Full Professor in the computer science and operations research department at U. Montreal, scientific director and founder of Mila and of IVADO, Turing Award 2018 recipient, Canada Research Chair in Statistical Learning Algorithms, as well as a Canada AI CIFAR Chair. He pioneered deep learning and has been getting the most citations per day in 2018 among all computer scientists, worldwide. He is an officer of the Order of Canada, member of the Royal Society of Canada, was awarded the Killam Prize, the Marie-Victorin Prize and the Radio-Canada Scientist of the year in 2017, and he is a member of the NeurIPS advisory board and co-founder of the ICLR conference, as well as program director of the CIFAR program on Learning in Machines and Brains. His goal is to contribute to uncover the principles giving rise to intelligence through learning, as well as favour the development of AI for the benefit of all.