MeMa - Methodology Matters
Seminar Series
Multicriteria objective programming applied to economic and environmental sustainability
Danilo Liuzzi
University of Cagliari
Room A, h. 10.00-13.00
NASP Graduate School in Social and Political Sciences
Via Pace, 10 - Milan
January 10, 2019
A stochastic dynamic multiobjective model for sustainable decision making
February 28, 2019
Planning sustainable development through a scenario-based stochastic goal programming model
March 28, 2019
A fuzzy goal programming model to analyze energy, environmental and sustainability goals of the United Arab
May 6, 2019
Sustainability and intertemporal equity: a multicriteria approach
l diagrams. However, it is very technically written.
Morgan, Stephen L. and Christopher Winship. (2007). Counterfactuals and Causal Inference: Methods and Principles of Social Research. Cambridge: Cambridge University Press.
Chapter 3 (p. 77-95) is a more applied discussion of directed graphs embedded in examples from sociology.
Robins, James M. (2001). “Data, Design, and Background Knowledge in Etiologic Inference,” Epidemiology 11 (3): 313-320.
Another less technical description of directed graphs.
Part II – Necessary and Unnecessary Controls or Closing the Backdoor
Time: 10:45-12:15
Recommended Literature
Morgan, Stephen L. and Christopher Winship. (2007). Counterfactuals and Causal Inference: Methods and Principles of Social Research. Cambridge: Cambridge University Press.
Chapter 3 (p. 95-130) is an applied discussion of the back-door criteria.
Pearl, J., Glymour, M., & Jewell, N. P. (2016). Causal inference in statistics: a primer. John Wiley & Sons.
Chapter 3 (p. 53-75).
Lunch Break 12:15-13:30
Part III – Effect Heterogeneity and the Average Treatment Effect
Time 13:30-15:00
Recommended Literature
Elwert, F., & Winship, C. (2010). Effect heterogeneity and bias in main-effects-only regression models. Heuristics, probability and causality: A tribute to Judea Pearl, 327-36.
Falleti, T. G., & Lynch, J. F. (2009). Context and causal mechanisms in political analysis. Comparative political studies, 9(42), 1143-1166.
Part IV – Interactions versus Structural Equations with Mediation Effects
Time 15:15-17:15
Recommended Literature
Pearl, J., Glymour, M., & Jewell, N. P. (2016). Causal inference in statistics: a primer. John Wiley & Sons.
Chapter 3 (p. 75-87).
Brambor, T., Clark, W. R., & Golder, M. (2006). Understanding interaction models: Improving empirical analyses. Political analysis, 14(1), 63-82.
Imai, K., Keele, L., & Yamamoto, T. (2010). Identification, inference and sensitivity analysis for causal mediation effects. Statistical Science, 1(25), 51-71.
