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Found 12 papers, 6 papers with code
Double/Debiased Machine Learning for Dynamic Treatment Effects
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.
Maximum Likelihood Estimation of Differentiated Products Demand Systems
We discuss estimation of the differentiated products demand system of Berry et al (1995) (BLP) by maximum likelihood estimation (MLE).
Estimating the Long-Term Effects of Novel Treatments
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.
Mostly Harmless Machine Learning: Learning Optimal Instruments in Linear IV Models
We offer straightforward theoretical results that justify incorporating machine learning in the standard linear instrumental variable setting.
Minimax Estimation of Conditional Moment Models
We develop an approach for estimating models described via conditional moment restrictions, with a prototypical application being non-parametric instrumental variable regression.
Double/Debiased Machine Learning for Dynamic Treatment Effects via g-Estimation
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 of the treated unit.
Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments
We develop a statistical learning approach to the estimation of heterogeneous effects, reducing the problem to the minimization of an appropriate loss function that depends on a set of auxiliary models (each corresponding to a separate prediction task).
Semi-Parametric Efficient Policy Learning with Continuous Actions
Our results also apply if the model does not satisfy our semi-parametric form, but rather we measure regret in terms of the best projection of the true value function to this functional space.
Non-Parametric Inference Adaptive to Intrinsic Dimension
We show that if the intrinsic dimension of the covariate distribution is equal to $d$, then the finite sample estimation error of our estimator is of order $n^{-1/(d+2)}$ and our estimate is $n^{1/(d+2)}$-asymptotically normal, irrespective of $D$.
Adversarial Generalized Method of Moments
We provide an approach for learning deep neural net representations of models described via conditional moment restrictions.
Deep IV: A Flexible Approach for Counterfactual Prediction
Counterfactual prediction requires understanding causal relationships between so-called treatment and outcome variables.
Counterfactual Prediction with Deep Instrumental Variables Networks
We are in the middle of a remarkable rise in the use and capability of artificial intelligence.
