33 citations · 121 across the 9 of their papers we have counts for
11 papers
Maximum Likelihood Estimation of Differentiated Products Demand Systems
Greg Lewis, Bora Ozaltun, Georgios Zervas
We discuss estimation of the differentiated products demand system of Berry et al (1995) (BLP) by maximum likelihood estimation (MLE). We derive the maximum likelihood estimator in…
Estimating the Long-Term Effects of Novel Treatments
Keith Battocchi, Eleanor Dillon, Maggie Hei +3
Policy makers typically face the problem of wanting to estimate the long-term effects of novel treatments, while only having historical data of older treatment options. We assume a…
Mostly Harmless Machine Learning: Learning Optimal Instruments in Linear IV Models
Jiafeng Chen, Daniel L. Chen, Greg Lewis
We offer straightforward theoretical results that justify incorporating machine learning in the standard linear instrumental variable setting. The key idea is to use machine learni…
Minimax Estimation of Conditional Moment Models
Nishanth Dikkala, Greg Lewis, Lester Mackey +1
We develop an approach for estimating models described via conditional moment restrictions, with a prototypical application being non-parametric instrumental variable regression. W…
Double/Debiased Machine Learning for Dynamic Treatment Effects via g-Estimation
Greg Lewis, Vasilis Syrgkanis
We consider the estimation of treatment effects in settings when multiple treatments are assigned over time and treatments can have a causal effect on future outcomes or the state…
Voluntary Disclosure and Personalized Pricing
S. Nageeb Ali, Greg Lewis, Shoshana Vasserman
Central to privacy concerns is that firms may use consumer data to price discriminate. A common policy response is that consumers should be given control over which firms access th…
Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments
Vasilis Syrgkanis, Victor Lei, Miruna Oprescu +3
We consider the estimation of heterogeneous treatment effects with arbitrary machine learning methods in the presence of unobserved confounders with the aid of a valid instrument.…
Semi-Parametric Efficient Policy Learning with Continuous Actions
Mert Demirer, Vasilis Syrgkanis, Greg Lewis +1
We consider off-policy evaluation and optimization with continuous action spaces. We focus on observational data where the data collection policy is unknown and needs to be estimat…
Non-Parametric Inference Adaptive to Intrinsic Dimension
Khashayar Khosravi, Greg Lewis, Vasilis Syrgkanis
We consider non-parametric estimation and inference of conditional moment models in high dimensions. We show that even when the dimension of the conditioning variable is larger…
Adversarial Generalized Method of Moments
Greg Lewis, Vasilis Syrgkanis
We provide an approach for learning deep neural net representations of models described via conditional moment restrictions. Conditional moment restrictions are widely used, as the…
Counterfactual Prediction with Deep Instrumental Variables Networks
Jason Hartford, Greg Lewis, Kevin Leyton-Brown +1
We are in the middle of a remarkable rise in the use and capability of artificial intelligence. Much of this growth has been fueled by the success of deep learning architectures: m…