Results 11 to 20 of about 157 (97)

Role of placebo samples in observational studies [PDF]

Journal of Causal Inference
In an observational study, it is common to leverage known null effects to detect bias. One such strategy is to set aside a placebo sample – a subset of data immune from the hypothesized cause-and-effect relationship. Existence of an effect in the placebo
Ye Ting, He Qijia, Chen Shuxiao, Zhang Bo   +3 more
doaj   +2 more sources

Conditional average treatment effect estimation with marginally constrained models [PDF]

Journal of Causal Inference, 2023
Treatment effect estimates are often available from randomized controlled trials as a single average treatment effect for a certain patient population.
van Amsterdam Wouter A. C., Ranganath Rajesh   +1 more
doaj   +2 more sources

Bias of the additive hazard model in the presence of causal effect heterogeneity. [PDF]

Lifetime Data Anal
Hazard ratios are prone to selection bias, compromising their use as causal estimands. On the other hand, if Aalen’s additive hazard model applies, the hazard difference has been shown to remain unaffected by the selection of frailty factors over time ...
Post RAJ, van den Heuvel ER, Putter H.
europepmc   +3 more sources

Quantifying the quality of configurational causal models [PDF]

Journal of Causal Inference
There is a growing number of studies benchmarking the performance of configurational comparative methods (CCMs) of causal data analysis. A core benchmark criterion used in these studies is a dichotomous (i.e., non-quantitative) correctness criterion ...
Baumgartner Michael, Falk Christoph
doaj   +2 more sources

The built-in selection bias of hazard ratios formalized using structural causal models. [PDF]

Lifetime Data Anal
It is known that the hazard ratio lacks a useful causal interpretation. Even for data from a randomized controlled trial, the hazard ratio suffers from so-called built-in selection bias as, over time, the individuals at risk among the exposed and ...
Post RAJ, van den Heuvel ER, Putter H.
europepmc   +3 more sources

On the Monotonicity of a Nondifferentially Mismeasured Binary Confounder

Journal of Causal Inference, 2020
Suppose that we are interested in the average causal effect of a binary treatment on an outcome when this relationship is confounded by a binary confounder. Suppose that the confounder is unobserved but a nondifferential proxy of it is observed.
Peña Jose M.
doaj   +2 more sources

A note on a sensitivity analysis for unmeasured confounding, and the related E-value

Journal of Causal Inference, 2020
Unmeasured confounding is one of the most important threats to the validity of observational studies. In this paper we scrutinize a recently proposed sensitivity analysis for unmeasured confounding.
Sjölander Arvid
doaj   +2 more sources

When is a Match Sufficient? A Score-based Balance Metric for the Synthetic Control Method

Journal of Causal Inference, 2020
In some applications, researchers using the synthetic control method (SCM) to evaluate the effect of a policy may struggle to determine whether they have identified a “good match” between the control group and treated group. In this paper, we demonstrate
Parast Layla, Hunt Priscillia, Griffin Beth Ann, Powell David   +3 more
doaj   +2 more sources

Estimating average causal effects with incomplete exposure and confounders [PDF]

Journal of Causal Inference
Standard methods for estimating average causal effects require complete observations of the exposure and confounders. In observational studies, however, missing data are ubiquitous. Motivated by a study on the effect of prescription opioids on mortality,
Wen Lan, McGee Glen
doaj   +2 more sources

Averaging causal estimators in high dimensions

Journal of Causal Inference, 2020
There has been increasing interest in recent years in the development of approaches to estimate causal effects when the number of potential confounders is prohibitively large.
Antonelli Joseph, Cefalu Matthew
doaj   +2 more sources