causal inference

Causal Fair Machine Learning via Rank-Preserving Interventional Distributions

Doubly Robust Estimation of Average Treatment Effects on the Treated through Marginal Structural Models

Causal evidence in health decision making: methodological approaches of causal inference and health decision science

Estimating the Effect of Central Bank Independence on Inflation Using Longitudinal Targeted Maximum Likelihood Estimation

Recently, there has been a lot of interest and discussion about the use of causal inference in economics. Whether it is feasible and whether there are benefits of working with a directed acyclic graph (DAG) and a non-parametric structural equation framework, is one aspect of the debate.

The impact of same-day antiretroviral therapy initiation under the WHO Treat-All policy

New Perspective for Soft Tissue Closure in Medication-Related Osteonecrosis of the Jaw (MRONJ) Using Barbed Sutures

Regression and Causality

The causal effect of an intervention (treatment/exposure) on an outcome can be estimated by: i) specifying knowledge about the data-generating process; ii) assessing under what assumptions a target quantity, such as for example a causal odds ratio, can be identified given the specified knowledge (and given the measured data); and then, iii) using appropriate statistical estimation techniques to estimate the desired parameter of interest.

The impact of delayed switch to second-line antiretroviral therapy on mortality, depending on failure time definition and CD4 count at failure

Increased Mortality with Delayed and Missed Switch to Second-Line Antiretroviral Therapy in South Africa

Regarding: Effect Estimates in Randomized Trials and Observational Studies: Comparing Apples with Apples