by Usha Govindarajulu | May 20, 2026 | Biostatistics, Blog, Usha Govindarajulu
May 20, 2026 Unmeasured confounding has been a long-standing methodological challenge for causal inference and these confounding mechanisms can violate the ignorability assumption that is a bedrock of causal inference. As they say, the profound implications of this...
by Usha Govindarajulu | Jan 2, 2025 | Biostatistics, Blog, Usha Govindarajulu
January 1, 2024 The authors were interested in the average treatment effect (ATE) which reflects how the treatment affects the potential outcome. In order to estimate ATE, propensity scores have been adapted for their estimation. such as the inverse probability...
by Usha Govindarajulu | Sep 25, 2024 | Biostatistics, Blog, Usha Govindarajulu
September 25, 2024 This article focused how changing living arrangements was associated with suicide risk using survival analysis along with causal inference. The authors admitted that traditional methods like Cox model analysis were not sufficient to handle...
by Usha Govindarajulu | Jun 20, 2024 | Blog, Usha Govindarajulu
The goals of this paper were to provide a set of conditions for inverse probability weights (IPW) to target a subpopulation of the patients who have clinical equipose, establish a relationship between matching weights and overlap weight estimators, and use the beta...
by Usha Govindarajulu | Jan 3, 2024 | Biostatistics, Blog, Usha Govindarajulu
January 3, 2023 In an article that appeared in Biometrical Journal, Le Bourdonnec et al (2023) discussed a method to address unmeasured confounding in cohort studies by an instrumental varible (IV) method for a time fixed expousre on an outcome trajectory, repeatedly...
