Identification of Causal Effects Using Instrumental Variables

Using Descendants as Instrumental Variables for the Identification of Direct Causal Effects in Linear SEMs

In this paper, we present an extended set of graphical criteria for the identification of direct causal effects in linear Structural Equation Models (SEMs). Previous methods of graphical identification of direct causal effects in linear SEMs include methods such as the single-door criterion, the instrumental variable and the IV-pair, and the accessory set. However, there remain graphical models...

Regression and Weighting Methods for Causal Inference Using Instrumental Variables

Recent researches in econometrics and statistics have gained considerable insights into the use of instrumental variables (IVs) for causal inference. A basic idea is that IVs serve as an experimental handle, the turning of which may change each individual’s treatment status and, through and only through this effect, also change observed outcome. The average difference in observed outcome relati...

Closed-loop Identification of Hammerstein Systems Using Iterative Instrumental Variables

A feedback connection of a Hammerstein system and a linear controller provides a good approach to modeling systems with actuator nonlinearity or input saturation during closed-loop experiments. This paper discusses closed-loop identification of Hammerstein systems where both input and output signals are correlated with noise, and time domain signals are related via a nonlinear map. The static n...

Identification of Causal Effects in Linear SEMs using the Instrumental Variable Function

In this paper, we discuss the problem of identification of causal effects in linear Structural Equation Models (SEMs) based on causal diagrams. The two main methods for solving this problem are algebraic methods, which attempts to solve the set of simultaneous equations based on Wright’s method of path analysis, and graphical methods, which tests for certain graphical criteria in the causal dia...

Identification of population average treatment effects using nonlinear instrumental variables estimators : another cautionary note