Combining Multiple Observational Data Sources to Estimate Causal Effects

Yang, Shu; Ding, Peng

doi:10.1080/01621459.2019.1609973

Cited by 57 publications

(43 citation statements)

References 86 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Such penalty functions can be potentially useful for handling high dimensional covariate problems. Also, the proposed method can be used for causal inference, including estimation of average treatment effect from observational studies (Morgan and Winship, 2014;Yang and Ding, 2020). Developing tools for causal inference using the kernel ridge regression-based propensity score method will be an important extension of this research.…”

Section: Discussionmentioning

confidence: 99%

Statistical inference using Regularized M-estimation in the reproducing kernel Hilbert space for handling missing data

Wang,

Kim

2021

Preprint

View full text Add to dashboard Cite

Imputation and propensity score weighting are two popular techniques for handling missing data. We address these problems using the regularized M-estimation techniques in the reproducing kernel Hilbert space. Specifically, we first use the kernel ridge regression to develop imputation for handling item nonresponse. While this nonparametric approach is potentially promising for imputation, its statistical properties are not investigated in the literature. Under some conditions on the order of the tuning parameter, we first establish the root-n consistency of the kernel ridge regression imputation estimator and show that it achieves the lower bound of the semiparametric asymptotic variance. A nonparametric propensity score estimator using the reproducing kernel Hilbert space is also developed by a novel application of the maximum entropy method for the density ratio function estimation. We show that the resulting propensity score estimator is asymptotically equivalent to the kernel ridge regression imputation estimator. Results from a limited simulation study are also presented to confirm our theory. The proposed method is applied to analyze the air pollution data measured in Beijing, China.

show abstract

Section: Discussionmentioning

confidence: 99%

Statistical inference using Regularized M-estimation in the reproducing kernel Hilbert space for handling missing data

Wang,

Kim

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Motivated by the observation that the treatment effects on the secondary outcome should be similar in the RCT and observational data if X is sufficient for Assumption 2, [45] developed a control function method for using differences in the estimated causal effects on the secondary outcome between the two samples to adjust estimation of the treatment effect on the primary outcome. [46] considered the scenario where a small validation dataset with all confounders (Y, A, X 1 , U, S = 1) and a big main dataset with unmeasured confounders (Y, A, X 1 , S = 0) are available. Both are random samples of the target population hence external validity is satisfied.…”

Section: Correcting For Bias In Observational Study Using Validation ...mentioning

confidence: 99%

Data Integration in Causal Inference

Xu¹,

Pan²,

Wang³

2021

Preprint

View full text Add to dashboard Cite

Integrating data from multiple heterogeneous sources has become increasingly popular to achieve a large sample size and diverse study population. This paper reviews development in causal inference methods that combines multiple datasets collected by potentially different designs from potentially heterogeneous populations. We summarize recent advances on combining randomized clinical trial with external information from observational studies or historical controls, combining samples when no single sample has all relevant variables with application to two-sample Mendelian randomization, distributed data setting under privacy concerns for comparative effectiveness and safety research using real-world data, Bayesian causal inference, and causal discovery methods.

show abstract

“…In presence of this type of auxiliary data, methods based on the generalized method of moment, generalized regression, weight calibration, constrained maximum likelihood, empirical likelihood, etc., have been proposed to borrow auxiliary information to power up the main study. [1][2][3][4][5][6][7][8][9][10][11][12][13] In this article, we consider a different type of auxiliary data that is also widely seen in applications, that is, an auxiliary measurement collected in the same study but served as the outcome in a secondary analysis. Usually, such kind of auxiliary measurement is highly associated with the primary outcome, and how to incorporate this secondary information to enhance estimation precision for the main analysis is of high interest.…”

Section: Introductionmentioning

confidence: 99%

Improving main analysis by borrowing information from auxiliary data

Chen

Han

2021

Statistics in Medicine

View full text Add to dashboard Cite

In many clinical and observational studies, auxiliary data from the same subjects, such as repeated measurements or surrogate variables, will be collected in addition to the data of main interest. Not directly related to the main study, these auxiliary data in practice are rarely incorporated into the main analysis, though they may carry extra information that can help improve the estimation in the main analysis. Under the setting where part of or all subjects have auxiliary data available, we propose an effective weighting approach to borrow the auxiliary information by building a working model for the auxiliary data, where improvement of estimation precision over the main analysis is guaranteed regardless of the specification of the working model. An information index is also constructed to assess how well the selected working model works to improve the main analysis. Both theoretical and numerical studies show the excellent and robust performance of the proposed method in comparison to estimation without using the auxiliary data. Finally, we utilize the Atherosclerosis Risk in Communities study for illustration.

show abstract

Combining Multiple Observational Data Sources to Estimate Causal Effects

Cited by 57 publications

References 86 publications

Statistical inference using Regularized M-estimation in the reproducing kernel Hilbert space for handling missing data

Statistical inference using Regularized M-estimation in the reproducing kernel Hilbert space for handling missing data

Data Integration in Causal Inference

Improving main analysis by borrowing information from auxiliary data

Contact Info

Product

Resources

About