ICRAF (Probabilistic Causal Models)

World Agroforestry Centre (ICRAF)

Probabilistic Causal Models for Nutrition Outcomes of Agricultural Actions

 

 

PI: Eike Luedeling, ICRAF

Collaborators: 
Keith Shepherd, Ramni Jamnadass, Christine Jost (all ICRAF)
Norman Fenton (Queen Mary, University of London)
Michael Krawinkel (University of Gießen)
Christian Borgemeister (Centre for Development Research)
Martin Neil (Agena Ltd)

Duration: 24 months (from 1 September 2015)

Value: £250,000

Countries of research: Kenya, UK, Germany

Summary of the project:

Knowledge gaps

Decision-makers in international development often struggle to allocate resources to where they are most effective because it is difficult to accurately predict the impact of interventions. They also often have difficulties in developing effective implementation plans, monitoring progress and evaluating project impacts. These difficulties are due to lack of data, complex impact pathways, and risks and uncertainties that are difficult to factor into intervention planning.

Proposed approach

Scientific approaches to produce reliable impact projections are rarely applied in agricultural development, but decision analysis techniques commonly used in other fields have potential to improve development decisions.

We propose to adapt such an approach to the needs of agriculture for nutrition interventions and apply it to two case studies.

The new method – causal models developed through a participatory process

The procedures we plan to develop will feature the construction of causal models – models that describe the mechanisms through which impact will be delivered – that are co-developed by experts, stakeholders and analysts through facilitated participatory processes.

Models will be formalised as Bayesian Networks (BNs), a modelling approach that has been widely applied in a range of disciplines, including medical sciences, genetics, environmental sciences, and legal reasoning. BNs allow formal representation of causal models, such as intervention impact pathways. They can work effectively with incomplete information, combine expert knowledge with other sources of information, and they allow adequate consideration of risk.

We will apply our approach in two case studies:

  1. introduction of fruit trees into agricultural landscapes of Eastern Kenya;
  2. conversion of traditional home garden systems in Uganda to large-scale staple crop production.

For both case studies, participatory workshops will convene experts on the systems, stakeholders involved in ongoing or prospective projects, and analysts skilled in facilitation and model building techniques.