# Two robust tools for inference about causal effects with invalid instruments

@article{Kang2020TwoRT, title={Two robust tools for inference about causal effects with invalid instruments}, author={Hyunseung Kang and Youjin Lee and Tianwen Tony Cai and Dylan S. Small}, journal={Biometrics}, year={2020}, volume={78}, pages={24 - 34} }

Instrumental variables have been widely used to estimate the causal effect of a treatment on an outcome. Existing confidence intervals for causal effects based on instrumental variables assume that all of the putative instrumental variables are valid; a valid instrumental variable is a variable that affects the outcome only by affecting the treatment and is not related to unmeasured confounders. However, in practice, some of the putative instrumental variables are likely to be invalid. This…

## 7 Citations

Causal Inference with Invalid Instruments: Post-selection Problems and A Solution Using Searching and Sampling

- Mathematics, Computer Science
- 2021

This paper constructs uniformly valid confidence intervals for the causal effect, which are robust to the mistakes in separating valid and invalid instruments, and compares their proposed methods with existing inference methods over a large set of simulation studies.

Post-selection Problems for Causal Inference with Invalid Instruments: A Solution Using Searching and Sampling

- Mathematics, Computer Science
- 2021

This paper constructs uniformly valid confidence intervals for the causal effect when the instruments are possibly invalid, and illustrates the post-selection problem of existing inference methods relying on instrument selection.

Semiparametric Efficient G-estimation with Invalid Instrumental Variables

- Mathematics, Economics
- 2021

The instrumental variable method is widely used in the health and social sciences for identiﬁcation and estimation of causal effects in the presence of potential unmeasured confounding. In order to…

Optimal tests of the composite null hypothesis arising in mediation analysis

- Mathematics
- 2021

The indirect effect of an exposure on an outcome through an intermediate variable can be identified by a product of regression coefficients under certain causal and regression modeling assumptions.…

Profile‐likelihood Bayesian model averaging for two‐sample summary data Mendelian randomization in the presence of horizontal pleiotropy

- EconomicsbioRxiv
- 2020

This work investigates the use of Bayesian model averaging (BMA) to preferentially search the space of models with the highest posterior likelihood and develops a bespoke Metropolis-Hasting algorithm to perform the search using the recently developed Robust Adjusted Profile Likelihood (MR-RAPS) as the basis for defining a posterior distribution.

Mendelian Randomization Test of Causal Effect Using High-Dimensional Summary Data

- MathematicsStatistica Sinica
- 2021

Mendelian randomization (MR) uses genetic variants as instrumental variables (IVs) to assess the causal effect of a risk factor on an outcome in the presence of unmeasured confounding. There is a…

Evidence factors from multiple, possibly invalid, instrumental variables

- EconomicsThe Annals of Statistics
- 2022

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