Application of Mathematical Optimization Procedures to Intervention   Effects in Structural Equation Models
  For a given statistical model, it often happens that it is necessary to intervene the model to reduce the variances of the output variables. In structural equation models, this can be done by changing the values of the path coefficients by intervention. First, we explain that the expectations and variance matrix can be decomposed into several parts in terms of the total effects. Then, we show that an algorithm to obtain intervention method which minimizes the weighted sum of the variances can be formulated as a convex quadratic programming. This formulation allows us to impose boundary conditions for the intervention, so that we can find the practical solutions. We also treat a problem to adjust the expectations on targets. 
Kentaro Tanaka, Atsushi Yagishita and Masami Miyakawa
stat.ME