RobustIV and controlfunctionIV: Causal Inference for Linear and Nonlinear Models with Invalid Instrumental Variables
Year: 2023
Type: article
Abstract: : We present R software packages RobustIV and controlfunctionIV for causal inference with possibly invalid instrumental variables. RobustIV focuses on the linear outcome model. It implements the two-stage hard thresholding method to select valid instrumental variables from a set of candidate instrumental variables and make inferences for the causal effect in both low- and high-dimensional settings. Furthermore, RobustIV implements the high-dimensional endogeneity test and the searching and sampling method, a uniformly valid inference method robust to errors in instrumental variable selection. controlfunctionIV considers the nonlinear outcome model and makes inferences about the causal effect based on the control function method. Our packages are demonstrated using two publicly available economic data sets together with applications to the Framingham Heart Study.
Source: Observational Studies
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: diamond
Awards: G1735617348, Predictive Modeling with High-Dimensional lncomplete Data, Repro Sampling Method: A Transformative Artificial-Sample-Based Inferential Framework with Applications to Discrete Parameter, High-Dimensional Data, and Rare Events Inferences, G250621538, G278200998, G3164675021, G36937976, G4976486911, Novel Designs and Methods to Remove Hidden Confounding Bias in Health Sciences, Inference in High-Dimensional Linear Models: Methods, Theory, and Applications, THE FRAMINGHAM HEART STUDY-N01HC25195-268025195-268025195, Semi-supervised Approaches to Denoising Electronic Health Records Data for Risk Prediction, G8683204365, G9000050004, G9000124674
OpenAlex ID: W4386519777