Identification and Estimation of the Bi-Directional MR with Some Invalid Instruments
- URL: http://arxiv.org/abs/2407.07933v2
- Date: Fri, 12 Jul 2024 15:15:58 GMT
- Title: Identification and Estimation of the Bi-Directional MR with Some Invalid Instruments
- Authors: Feng Xie, Zhen Yao, Lin Xie, Yan Zeng, Zhi Geng,
- Abstract summary: We consider the challenging problem of estimating causal effects from purely observational data in the bi-directional Mendelian randomization (MR)
In this paper, we first theoretically investigate the identification of the bi-directional MR from observational data.
We develop a cluster fusion-like method to discover valid IV sets and estimate the causal effects of interest.
- Score: 10.332963283207777
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We consider the challenging problem of estimating causal effects from purely observational data in the bi-directional Mendelian randomization (MR), where some invalid instruments, as well as unmeasured confounding, usually exist. To address this problem, most existing methods attempt to find proper valid instrumental variables (IVs) for the target causal effect by expert knowledge or by assuming that the causal model is a one-directional MR model. As such, in this paper, we first theoretically investigate the identification of the bi-directional MR from observational data. In particular, we provide necessary and sufficient conditions under which valid IV sets are correctly identified such that the bi-directional MR model is identifiable, including the causal directions of a pair of phenotypes (i.e., the treatment and outcome). Moreover, based on the identification theory, we develop a cluster fusion-like method to discover valid IV sets and estimate the causal effects of interest. We theoretically demonstrate the correctness of the proposed algorithm. Experimental results show the effectiveness of our method for estimating causal effects in bi-directional MR.
Related papers
- Estimating Treatment Effects with Independent Component Analysis [30.83679633039883]
We show that Independent Component Analysis (ICA) can be used for causal effect estimation in the partially linear regression (PLR) model.<n>We find that linear ICA can accurately estimate multiple treatment effects even in the presence of Gaussian confounders or nonlinear nuisance.
arXiv Detail & Related papers (2025-07-22T11:16:23Z) - Causal Effect Identification in lvLiNGAM from Higher-Order Cumulants [20.751445296400316]
This paper investigates causal effect identification in latent variable Linear Non-Gaussian Acyclic Models (lvLiNGAM) using higher-order cumulants.<n>We prove that causal effects are identifiable with a single proxy or instrument and provide corresponding estimation methods.
arXiv Detail & Related papers (2025-06-05T16:14:35Z) - Data Fusion for Partial Identification of Causal Effects [62.56890808004615]
We propose a novel partial identification framework that enables researchers to answer key questions.<n>Is the causal effect positive or negative? and How severe must assumption violations be to overturn this conclusion?<n>We apply our framework to the Project STAR study, which investigates the effect of classroom size on students' third-grade standardized test performance.
arXiv Detail & Related papers (2025-05-30T07:13:01Z) - Distributional Instrumental Variable Method [4.34680331569334]
The aim of this work is to estimate the entire interventional distribution.
We propose a method called Distributional Instrumental Variable (DIV), which uses generative modelling in a nonlinear IV setting.
arXiv Detail & Related papers (2025-02-11T15:33:06Z) - Double Machine Learning meets Panel Data -- Promises, Pitfalls, and Potential Solutions [0.0]
Estimating causal effect using machine learning (ML) algorithms can help to relax functional form assumptions if used within appropriate frameworks.
We show how we can adapt machine learning (DML) for panel data in the presence of unobserved heterogeneity.
We also show that the influence of the unobserved heterogeneity on the observed confounders plays a significant role for the performance of most alternative methods.
arXiv Detail & Related papers (2024-09-02T13:59:54Z) - Smoke and Mirrors in Causal Downstream Tasks [59.90654397037007]
This paper looks at the causal inference task of treatment effect estimation, where the outcome of interest is recorded in high-dimensional observations.
We compare 6 480 models fine-tuned from state-of-the-art visual backbones, and find that the sampling and modeling choices significantly affect the accuracy of the causal estimate.
Our results suggest that future benchmarks should carefully consider real downstream scientific questions, especially causal ones.
arXiv Detail & Related papers (2024-05-27T13:26:34Z) - Simultaneous inference for generalized linear models with unmeasured confounders [0.0]
We propose a unified statistical estimation and inference framework that harnesses structures and integrates linear projections into three key stages.
We show effective Type-I error control of $z$-tests as sample and response sizes approach infinity.
arXiv Detail & Related papers (2023-09-13T18:53:11Z) - A Causal Framework for Decomposing Spurious Variations [68.12191782657437]
We develop tools for decomposing spurious variations in Markovian and Semi-Markovian models.
We prove the first results that allow a non-parametric decomposition of spurious effects.
The described approach has several applications, ranging from explainable and fair AI to questions in epidemiology and medicine.
arXiv Detail & Related papers (2023-06-08T09:40:28Z) - Nonparametric Identifiability of Causal Representations from Unknown
Interventions [63.1354734978244]
We study causal representation learning, the task of inferring latent causal variables and their causal relations from mixtures of the variables.
Our goal is to identify both the ground truth latents and their causal graph up to a set of ambiguities which we show to be irresolvable from interventional data.
arXiv Detail & Related papers (2023-06-01T10:51:58Z) - Data-Driven Estimation of Heterogeneous Treatment Effects [15.140272661540655]
Estimating how a treatment affects different individuals, known as heterogeneous treatment effect estimation, is an important problem in empirical sciences.
We provide a survey of state-of-the-art data-driven methods for heterogeneous treatment effect estimation using machine learning.
arXiv Detail & Related papers (2023-01-16T21:36:49Z) - NESTER: An Adaptive Neurosymbolic Method for Causal Effect Estimation [37.361149306896024]
Causal effect estimation from observational data is a central problem in causal inference.
We propose an adaptive method called Neurosymbolic Causal Effect Estimator (NESTER)
Our comprehensive empirical results show that NESTER performs better than state-of-the-art methods on benchmark datasets.
arXiv Detail & Related papers (2022-11-08T16:48:46Z) - Causal Effect Estimation using Variational Information Bottleneck [19.6760527269791]
Causal inference is to estimate the causal effect in a causal relationship when intervention is applied.
We propose a method to estimate Causal Effect by using Variational Information Bottleneck (CEVIB)
arXiv Detail & Related papers (2021-10-26T13:46:12Z) - Estimation of Bivariate Structural Causal Models by Variational Gaussian
Process Regression Under Likelihoods Parametrised by Normalising Flows [74.85071867225533]
Causal mechanisms can be described by structural causal models.
One major drawback of state-of-the-art artificial intelligence is its lack of explainability.
arXiv Detail & Related papers (2021-09-06T14:52:58Z) - Correct block-design experiments mitigate temporal correlation bias in
EEG classification [68.85562949901077]
We show that the main claim in [1] is drastically overstated and their other analyses are seriously flawed by wrong methodological choices.
We investigate the influence of EEG temporal correlation on classification accuracy by testing the same models in two additional experimental settings.
arXiv Detail & Related papers (2020-11-25T22:25:21Z) - A Critical View of the Structural Causal Model [89.43277111586258]
We show that one can identify the cause and the effect without considering their interaction at all.
We propose a new adversarial training method that mimics the disentangled structure of the causal model.
Our multidimensional method outperforms the literature methods on both synthetic and real world datasets.
arXiv Detail & Related papers (2020-02-23T22:52:28Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.