Observational studyStatistics in medicine2022
Introduction to computational causal inference using reproducible Stata, R, and Python code: A tutorial.
Observational study in Statistics in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
41 citing papers in PubMed, 70 citations in OpenAlex.
- Rituximab Maintenance Added to Ibrutinib-Containing Therapy in Younger, Untreated Patients With Mantle Cell Lymphoma: Results From the TRIANGLE Trial.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2026Trial
- Protective effect of intensive glucose lowering therapy on all-cause mortality, adjusted for treatment switching using G-estimation method, the ACCORD trial.Scientific reports · 2023Trial
- Within-household transmission risk of pulmonary tuberculosis in the era of universal antiretroviral therapy.medRxiv : the preprint server for health sciences · 2026Article
- Marginal causal effect of fungicide seed treatments on soybean yield and uncertain profitability in the US Midwest.Scientific reports · 2026Article
- Prenatal paracetamol exposure and wheezing in infancy: a targeted maximum likelihood estimation application.BMJ open respiratory research · 2026Article
- Exacerbation of racial disparities in COVID-19 outcomes by Alzheimer's disease and related dementias among nursing home residents.American journal of epidemiology · 2026Article
- Effects of a post-acute care management program on subsequent acute care utilization for hospitalized patients discharged to skilled nursing facilities.BMC health services research · 2026Article
- Comparative Effectiveness of Laparoscopic versus Open Colectomy on 5-Year Overall Survival in Non-Metastatic Colon Cancer: A Target Trial Emulation Study Using Retrospective Cohort Data from a Cancer Registry.Clinical epidemiology · 2026Article
- Critical Appraisal of Causality Observational Studies: A Case-Study of the Effect of Tattoo on Malignant Lymphoma.International journal of preventive medicine · 2026Article
- Time to antiviral treatment in mild-moderate COVID-19 in the emergency department: influence of prescribing physician and effect on outcomes.Internal and emergency medicine · 2026Article
- Towards robust causal inference in epidemiologic research: employing double cross-fit TMLE in right heart catheterization data.American journal of epidemiology · 2025Article
- Performance of Cross-Validated Targeted Maximum Likelihood Estimation.Statistics in medicine · 2025Article
- Relationship Difficulty Is Associated With Poorer Mental Health in Later Life.The journals of gerontology. Series B, Psychological sciences and social sciences · 2025Article
- On the Uses and Abuses of Regression Models: A Call for Reform of Statistical Practice and Teaching.Statistics in medicine · 2025Article
- Interaction between opium use and cigarette smoking on bladder cancer: An inverse probability weighting approach based on a multicenter case-control study in Iran.Global epidemiology · 2025Article
- Estimating marginal structural model parameters for time-fixed, binary actions with g-computation and estimating equations.American journal of epidemiology · 2025Article
- Quantification of racial and ethnic disparities in alcohol-related problems in light of different methodological approaches.Addiction (Abingdon, England) · 2025Article
- Guidelines and Best Practices for the Use of Targeted Maximum Likelihood and Machine Learning When Estimating Causal Effects of Exposures on Time-To-Event Outcomes.Statistics in medicine · 2025Article
- So Many Choices: A Guide to Selecting Among Methods to Adjust for Observed Confounders.Statistics in medicine · 2025Article
- Comparative effectiveness of laparoscopic versus open colectomy in colon cancer patients: a study protocol for emulating a target trial using cancer registry data.Journal of cancer research and clinical oncology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 4 institutions in 4 countries.
Funding
Abstract
The main purpose of many medical studies is to estimate the effects of a treatment or exposure on an outcome. However, it is not always possible to randomize the study participants to a particular treatment, therefore observational study designs may be used. There are major challenges with observational studies; one of which is confounding. Controlling for confounding is commonly performed by direct adjustment of measured confounders; although, sometimes this approach is suboptimal due to modeling assumptions and misspecification. Recent advances in the field of causal inference have dealt with confounding by building on classical standardization methods. However, these recent advances have progressed quickly with a relative paucity of computational-oriented applied tutorials contributing to some confusion in the use of these methods among applied researchers. In this tutorial, we show the computational implementation of different causal inference estimators from a historical perspective where new estimators were developed to overcome the limitations of the previous estimators (ie, nonparametric and parametric g-formula, inverse probability weighting, double-robust, and data-adaptive estimators). We illustrate the implementation of different methods using an empirical example from the Connors study based on intensive care medicine, and most importantly, we provide reproducible and commented code in Stata, R, and Python for researchers to adapt in their own observational study. The code can be accessed at https://github.com/migariane/Tutorial_Computational_Causal_Inference_Estimators.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.