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Additive models play an essential role in studying non-linear relationships.Despite many recent advances in estimation, there is a lack of methods andtheories for inference in high-dimensional additive models, includingconfidence interval construction and hypothesis testing. Motivated by inferencefor non-linear treatment effects, we consider the high-dimensional additivemodel and make inference for the derivative of the function of interest. Wepropose a novel decorrelated local linear estimator and establish itsasymptotic normality. The main novelty is the construction of the decorrelationweights, which is instrumental in reducing the error inherited from estimatingthe nuisance functions in the high-dimensional additive model. We construct theconfidence interval for the function derivative and conduct the relatedhypothesis testing. We demonstrate our proposed method over large-scalesimulation studies and apply it to identify non-linear effects in the motifregression problem. Our proposed method is implemented in the R package\texttt{DLL} available from CRAN.

Decorrelated Local Linear Estimator: Inference for Non-linear Effects in High-dimensional Additive Models