Automatic Double Machine Learning for Continuous Treatment Effects
In this paper, we introduce and prove asymptotic normality for a new nonparametric estimator of continuous treatment effects. Specifically, we estimate the average dose-response function - the expected value of an outcome of interest at a particular level of the treatment level. We utilize tools from both the double debiased machine learning (DML) and the automatic double machine learning (ADML) literatures to construct our estimator. Our estimator utilizes a novel debiasing method that leads to nice theoretical stability and balancing properties. In simulations our estimator performs well compared to current methods.
Code (0)
등록된 구현이 없습니다.
Tasks
BIG-bench Machine LearningSimilar Papers 제목 키워드 기반
Continuous difference-in-differences with double/debiased machine learning
This paper extends difference-in-differences to settings involving continuous treatments. Specifically, the average treatment effect on the treated (ATT) at any level of continuous treatment intensity is identified using…
Double Debiased Machine Learning Nonparametric Inference with Continuous Treatments
We propose a doubly robust inference method for causal effects of continuous treatment variables, under unconfoundedness and with nonparametric or high-dimensional nuisance functions. Our double debiased machine learning…
BIG-bench Machine LearningLee Bounds with a Continuous Treatment in Sample Selection
We study causal inference in sample selection models where a continuous or multivalued treatment affects both outcomes and their observability (e.g., employment or survey responses). We generalized the widely used Lee (2…
Causal InferenceSelection biasDouble Machine Learning Density Estimation for Local Treatment Effects with Instruments
It is common to quantify causal effects with mean values, which, however, may fail to capture significant distribution differences of the outcome under different treatments. We study the problem of estimating the density…
BIG-bench Machine LearningDensity EstimationDouble Debiased Machine Learning for Mediation Analysis with Continuous Treatments
Uncovering causal mediation effects is of significant value to practitioners seeking to isolate the direct treatment effect from the potential mediated effect. We propose a double machine learning (DML) algorithm for med…