Looking for Cues of Project Impact: The Role of Causal Link Monitoring

By Ashley Strahley, MPH. This blog post discusses how the Data for Impact project is using causal link monitoring to keep on track.

by Ashley Strahley, MPH

I recently moved to a new house and started a longer commute to and from work. It’s changed my habits in a few ways. I’ve become more efficient at getting ready and getting out the door in the mornings, I’ve started listening to more podcasts and audio books, and I’m much more tuned in to cues that let me know how I’m progressing in my drive. Highway exit numbers count down the miles until I get to work or back home, and I now pay attention to landmarks along the way—a funny sign, an eye-catching building—that let me know I’m 10 or 15 minutes away.

I also tune into cues of progress in my work as a monitoring and evaluation (M&E) officer, keeping track of the progress and achievements of global health projects at the University of North Carolina at Chapel Hill. We have a new project, Data for Impact (D4I), funded by the United States Agency for International Development (USAID). It focuses on strengthening the capacity of organizations in low-resource countries to generate and use data to improve health programs and policies. I am part of a team using a new project design and M&E approach to keep D4I on track—causal link monitoring (CLM).

CLM was developed by Heather Britt, Richard Hummelbrunner, and Jacqueline Greene.[1] It’s a way for a project to identify what needs to happen and who needs to be involved to achieve results, and then check periodically to ensure that it moves forward as planned. CLM improves on traditional M&E by providing richer information about progress sooner—which means that information is available sooner to adjust our work. This iterative way to make things better reflects USAID’s Collaborating, Learning, and Adapting (CLA) framework and a global drive for intentional and continuous project learning and adaptive management.

With D4I, we started with a logic model and then applied CLM to describe what needs to happen for us to move along the path from inputs to outputs to outcomes. At each step, we ask, “Who is responsible?” and “What needs to be done?”

For example, if we are generating a data set on mother-to-child HIV transmission in Malawi and we want to make it available to stakeholders, we ask, “Who needs these data?” and “What do they need to do so the data are useful for improving programs or policies?” Thinking this way helps keep us focused on impact and on coordination with partners and stakeholders. And, by identifying processes that move us toward impact, we get a clear idea of what to look for—landmarks, if you will.

As the project picks up speed, we plan to use CLM to initiate regular pause-and-reflect moments—opportunities for sharing the successes and challenges that accompany progress—to ensure we never stop learning.

I occasionally come across especially bad traffic on my drive, and I have to decide to stay the course or change my route. I do this by using the cues and landmarks I’ve identified—am I close or far away from where I’m going? D4I may encounter a setback here or there, like any project, but CLM will help us adapt quickly and get back on track. And this means getting to meaningful impact for our partners sooner—speeding up the timeline for them to generate and use data to improve health programs and policies.

[1] See https://www.betterevaluation.org/sites/default/files/CLM%20Brief_20170615_1528%20FINAL.pdf

Republished from the Evaluate blog.

Filed under: D4I , Data for Impact , Data
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