46 citations · 79 across the 16 of their papers we have counts for
16 papers
Graph-based causal variance decompositions: When "variance explained" means causation
Olli Saarela, Juha Karvanen
Recursive application of the law of total variance decomposes the marginal variance of an outcome into components attributed to explanatory variables and a residual component. The…
Causal Variance Decompositions for Measuring Health Inequalities
Lin Yu, Zhihui Liu, Kathy Han +1
Recent causal inference literature has introduced causal effect decompositions to quantify sources of observed inequalities or disparities in outcomes, but these approaches are typ…
A Bayesian joint model for mediation analysis with matrix-valued mediators
Zijin Liu, Zhihui Liu, Ali Hosni +3
Unscheduled treatment interruptions may lead to reduced quality of care in radiation therapy (RT). Identifying the RT prescription dose effects on the outcome of treatment interrup…
Multiply Robust Estimator Circumvents Hyperparameter Tuning of Neural Network Models in Causal Inference
Mehdi Rostami, Olli Saarela
Estimation of the Average Treatment Effect (ATE) is often carried out in 2 steps, wherein the first step, the treatment and outcome are modeled, and in the second step the predicti…
A marginal structural model for normal tissue complication probability
Thai-Son Tang, Zhihui Liu, Ali Hosni +2
The goal of radiation therapy for cancer is to deliver prescribed radiation dose to the tumor while minimizing dose to the surrounding healthy tissues. To evaluate treatment plans,…
The Bias-Variance Tradeoff of Doubly Robust Estimator with Targeted regularized Neural Networks Predictions
Mehdi Rostami, Olli Saarela, Michael Escobar
The Doubly Robust (DR) estimation of ATE can be carried out in 2 steps, where in the first step, the treatment and outcome are modeled, and in the second step the predictions are i…
Normalized Augmented Inverse Probability Weighting with Neural Network Predictions
Mehdi Rostami, Olli Saarela
The estimation of Average Treatment Effect (ATE) as a causal parameter is carried out in two steps, where in the first step, the treatment and outcome are modeled to incorporate th…
casebase: An Alternative Framework For Survival Analysis and Comparison of Event Rates
Sahir Rai Bhatnagar, Maxime Turgeon, Jesse Islam +2
In epidemiological studies of time-to-event data, a quantity of interest to the clinician and the patient is the risk of an event given a covariate profile. However, methods relyin…
Estimation of marriage incidence rates by combining two cross-sectional retrospective designs: Event history analysis of two dependent processes
Sangita Kulathinal, Minna Säävälä, Kari Auranen +1
The aim of this work is to develop methods for studying the determinants of marriage incidence using marriage histories collected under two different types of retrospective cross-s…
Causal mediation analysis decomposition of between-hospital variance
Bo Chen, Keith A. Lawson, Antonio Finelli +1
Causal variance decompositions for a given disease-specific quality indicator can be used to quantify differences in performance between hospitals or health care providers. While v…
Bayesian non-parametric ordinal regression under a monotonicity constraint
Olli Saarela, Christian Rohrbeck, Elja Arjas
Compared to the nominal scale, the ordinal scale for a categorical outcome variable has the property of making a monotonicity assumption for the covariate effects meaningful. This…
The role of exchangeability in causal inference
Olli Saarela, David A. Stephens, Erica E. M. Moodie
Though the notion of exchangeability has been discussed in the causal inference literature under various guises, it has rarely taken its original meaning as a symmetry property of…
Hierarchical causal variance decomposition for institution and provider comparisons in healthcare
Bo Chen, Olli Saarela
Disease-specific quality indicators (QIs) are used to compare institutions and health care providers in terms processes or outcomes relevant to treatment of a particular condition.…
Instrumental variable estimation of early treatment effect in randomized screening trials
Sudipta Saha, Zhihui Liu, Olli Saarela
The primary analysis of randomized screening trials for cancer typically adheres to the intention-to-screen principle, measuring cancer-specific mortality reductions between screen…
Causal variance decompositions for institutional comparisons in healthcare
Bo Chen, Keith A. Lawson, Antonio Finelli +1
There is increasing interest in comparing institutions delivering healthcare in terms of disease-specific quality indicators (QIs) that capture processes or outcomes showing variat…
A Bayesian view of doubly robust causal inference
Olli Saarela, Léo R. Belzile, David A. Stephens
In causal inference confounding may be controlled either through regression adjustment in an outcome model, or through propensity score adjustment or inverse probability of treatme…