Analyzing Individual and Average Causal Effects via Structural Equation Models

By Rolf Steyer. 2005

66 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/e1a0998958bc05c866c08726a4b091bbf76a7e49

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Management Science and Operations Research

1. K. Kraus
2. S. Pohl
3. T. D. Cook
4. Rolf Steyer
5. W. Shadish

In Statistics and Probability

1. W. Shadish
2. Peter M. Steiner
3. T. D. Cook
4. K. Kraus
5. S. Pohl
6. Rolf Steyer
7. Guido W. Imbens
8. John Antonakis

Citations in the corpus

• Modelling method effects as individual causal effects (2007, background): “In this tradition, the individual causal effect of treatment j compared with treatment 1 for unit u is defined as the difference τj.u/− τ1.u/ in the true scores of u under treatment j and under treatment 1 (see, for example, Steyer (2005)).”
• On the Importance of Reliable Covariate Measurement in Selection Bias Adjustments Using Propensity Scores (2011, background): “Structural equation modeling either of the PS or outcome model solves the problem but needs at least two measurements for each latent construct (Kaplan, 1999; Steyer, 2005).”