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Balance Regularized Neural Network Models for Causal Effect Estimation
Estimating individual and average treatment effects from observational data is an important problem in many domains such as healthcare and e-commerce.
Amortized learning of neural causal representations
The CRN represent causal models using continuous representations and hence could scale much better with the number of variables.
Towards Verified Robustness under Text Deletion Interventions
Neural networks are widely used in Natural Language Processing, yet despite their empirical successes, their behaviour is brittle: they are both over-sensitive to small input changes, and under-sensitive to deletions of large fractions of input text.
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