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MeMa - Multicriteria objective programming applied to economic and enviromental sustainability

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.

 

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