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Scalable Algorithms for Nonlinear Causal Inference
Gokul Swamy · Sanjiban Choudhury · James Bagnell · Steven Wu

We derive and implement nonlinear extensions of the classical instrumental variable regression (IVR) technique. Our key insight is that even in the nonlinear setting, finding a causally consistent estimate of a structural equation is equivalent to satisfying constraints on conditional outcome moments. This insight allows us to leverage standard constrained optimization techniques to reframe the work of Dikkala et al. as optimizing a regularized Lagrangian and reveal underlying smoothness assumptions. We then propose a new algorithm, CausAL, that instead optimizes an augmented Lagrangian, requiring a different definition of smoothness and no adversarial training. We then extend our method to handle matching outcome distributions instead of just expected values, propose an efficient no-regret procedure, and implement a practical realization via a modification of an Integral Probability Metric (IPM) GAN which we call ACADIMI.

Author Information

Gokul Swamy (Carnegie Mellon University)
Sanjiban Choudhury (Aurora)
James Bagnell (Aurora Innovation)

Drew has worked for two decades at the intersection of machine learning and robotics both as a faculty member at Carnegie Mellon University and in engagements with industry from self-driving haul trucks to perception architecture for Uber’s self-driving cars and in his current role as CTO of Aurora Innovation.

Steven Wu (Carnegie Mellon University)

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