# TSCI: two stage curvature identification for causal inference with invalid instruments

arXiv: https://arxiv.org/abs/2304.00513 · HTML: https://mlsanity.com/paper/2304.00513/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 31 references that are in the map.
- First posted on arXiv 2 Apr 2023.

## Builds on (most cited first, 6 of 6)

- [DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R](https://mlsanity.com/paper/2103.09603/) (2021)
- [The GENIUS Approach to Robust Mendelian Randomization Inference](https://mlsanity.com/paper/1709.07779/) (2017)
- [Regularizing Double Machine Learning in Partially Linear Endogenous Models](https://mlsanity.com/paper/2101.12525/) (2021)
- [Causal Inference with Invalid Instruments: Post-selection Problems and A Solution Using Searching and Sampling](https://mlsanity.com/paper/2104.06911/) (2021)
- [Causal Inference for Nonlinear Outcome Models with Possibly Invalid Instrumental Variables](https://mlsanity.com/paper/2010.09922/) (2020)
- [RobustIV and controlfunctionIV: Causal Inference for Linear and Nonlinear Models with Invalid Instrumental Variables](https://mlsanity.com/paper/2301.04412/) (2023)

## Abstract (arXiv)

TSCI implements treatment effect estimation from observational data under invalid instruments in the R statistical computing environment. Existing instrumental variable approaches rely on arguably strong and untestable identification assumptions, which limits their practical application. TSCI does not require the classical instrumental variable identification conditions and is effective even if all instruments are invalid. TSCI implements a two-stage algorithm. In the first stage, machine learning is used to cope with nonlinearities and interactions in the treatment model. In the second stage, a space to capture the instrument violations is selected in a data-adaptive way. These violations are then projected out to estimate the treatment effect.