Complier average causal effect estimation is a methodology that estimates the effect of complying with an intervention.
We use our approach to develop new methods for three statistical tasks: nonparametric quantile regression, conditional average partial effect estimation and heterogeneous treatment effect estimation via instrumental variables.
Bowden, J., Davey Smith, G. & Burgess, S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression.
Additionally, asymmetric price responses are specified in the model for the rebound effect estimation.
A broad empirical literature uses "event study" research designs for treatment effect estimation, a setting in which all units in the panel receive treatment but at random times.
The synthetic control method allows for effect estimation in settings where a single unit (a state, country, firm, etc). is exposed to an event or intervention.
The influence of wind speed masking on the wind effect estimation is discussed.
Combined effect estimation was computed with fixed- and random-effect models; a heterogeneity test was also applied.
Blocking is commonly used in design of experiments to reduce systematic variation and increase precision of effect estimation.
As this bias is common, we sought to determine whether it always leads to biased effect estimation.
Here, we also use an additional technique for treatment effect estimation known as inverse probability weighted regression adjustment, IPWRA.
