Pages that link to "Item:Q1431156"

The following pages link to Confounding and collapsibility in causal inference (Q1431156):

Displaying 50 items.

• Analysis of 1:1 matched cohort studies and twin studies, with binary exposures and binary outcomes (Q252785) (← links)
• Structural nested models and G-estimation: the partially realized promise (Q252829) (← links)
• Model misspecification when excluding instrumental variables from PS models in settings where instruments modify the effects of covariates on treatment (Q306804) (← links)
• Likelihood reweighting methods to reduce potential bias in noninferiority trials which rely on historical data to make inference (Q386771) (← links)
• The logic of Simpson's paradox (Q411576) (← links)
• On instrumental variables estimation of causal odds ratios (Q449850) (← links)
• Relations among homogeneity, collapsibility and nonconfounding in distribution effects (Q551443) (← links)
• Covariate selection for identifying the effects of a particular type of conditional plan using causal networks (Q610737) (← links)
• Conditions for uniformly non-confounding of causal distribution effects over multiple covariates (Q625684) (← links)
• On collapsibility and confounding bias in Cox and Aalen regression models (Q746469) (← links)
• Heterogeneous indirect effects for multiple mediators using interventional effect models (Q829924) (← links)
• Causal network learning with non-invertible functional relationships (Q830445) (← links)
• A new variant of the parallel regression model with variable selection in surveys with sensitive attribute (Q830681) (← links)
• Matching methods for causal inference: a review and a look forward (Q903298) (← links)
• Graphical models for inference under outcome-dependent sampling (Q906534) (← links)
• Dimension reduction summaries for balanced contrasts (Q958817) (← links)
• Causal inference in statistics: an overview (Q975575) (← links)
• Detecting multiple confounders (Q1007488) (← links)
• Estimating high-dimensional intervention effects from observational data (Q1043733) (← links)
• The foundations of confounding in epidemiology (Q1104676) (← links)
• Confounding and collapsibility in causal inference (Q1431156) (← links)
• From association to causation: Some remarks on the history of statistics. (Q1431169) (← links)
• Choice as an alternative to control in observational studies. (With comments and a rejoinder). (Q1431170) (← links)
• On collapsibilities of Yule's measure (Q1609688) (← links)
• Statistics and causal inference: A review. (With discussion) (Q1875689) (← links)
• On the definition of a confounder (Q1952448) (← links)
• Compartmental model diagrams as causal representations in relation to DAGs (Q2001850) (← links)
• Alternative sensitivity analyses for regression estimates of treatment effects to unobserved confounding in binary and survival data (Q2082465) (← links)
• Exact parametric causal mediation analysis for a binary outcome with a binary mediator (Q2125970) (← links)
• The magnitude and direction of collider bias for binary variables (Q2192289) (← links)
• Instrumental variable estimation with the R package \texttt{vtools} (Q2192298) (← links)
• Regression analysis of unmeasured confounding (Q2197498) (← links)
• Causal impact of masks, policies, behavior on early Covid-19 pandemic in the U.S. (Q2224898) (← links)
• The choice of effect measure for binary outcomes: introducing counterfactual outcome state transition parameters (Q2324999) (← links)
• Mediation analysis with attributable fractions (Q2325000) (← links)
• Automated versus do-it-yourself methods for causal inference: lessons learned from a data analysis competition (Q2325609) (← links)
• Confounding, homogeneity and collapsibility for causal effects in epidemiologic studies. (Q2714945) (← links)
• Accounting for uncertainty in confounder and effect modifier selection when estimating average causal effects in generalized linear models (Q2803486) (← links)
• Confounding and effect modification: distribution and measure (Q2867337) (← links)
• Identification of Confounding versus Dispersing Covariates by Confounding Influence (Q2873950) (← links)
• Adjustments and their consequences -- Collapsibility analysis using graphical models (Q2889637) (← links)
• Propensity scores: from naïve enthusiasm to intuitive understanding (Q2894112) (← links)
• Estimating and contextualizing the attenuation of odds ratios due to non collapsibility (Q2980136) (← links)
• On quantifying the magnitude of confounding (Q3303579) (← links)
• A two-stage strategy to accommodate general patterns of confounding in the design of observational studies (Q3303814) (← links)
• Point and Interval Estimations of Marginal Risk Difference by Logistic Model (Q3458108) (← links)
• Rotation‐based multiple testing in the multivariate linear model (Q3465373) (← links)
• The Performance of Two Data-Generation Processes for Data with Specified Marginal Treatment Odds Ratios (Q3527739) (← links)
• Estimating a Marginal Causal Odds Ratio Subject to Confounding (Q3622049) (← links)
• Distortion of effects caused by indirect confounding (Q3631477) (← links)