Na Kweku Opoku-Agyemang

We dey predict plenty things—climate patterns, disease risks, school results and others. Artificial Intelligence (AI) don hurry this trend by finding patterns inside plenty data and using am to make deeper, better predictions.

But one question dey wey prediction alone no fit answer:

To know wetin go happen na one thing. To know wetin go happen if we interfere na another thing.

That difference get big meaning for Africa, because development no be only to sabi wetin fit happen. E dey about how to change real situation so things go better.

Make we reason example: for one town umbrella sales and road accidents dey increase when e dey rain. Just because both things dey happen at the same time no mean say one cause the other. Umbrella no be wetin dey cause accident. Na rain dey affect both.

Lesson clear. But many development problems no clear like that: two things fit move together for real reasons without one causing the other. The harder question be wetin go happen if we change one of dem.

For development, relationship dey every time. Countries wey invest more for education fit grow faster or e fit no change. Areas wey get better roads fit get higher incomes or fit no. Policymakers need decide where to put resources so e go bring better results.

But to just observe say two things dey together no go tell us wetin for happen if we change one.

Na there causal arguments dey important. One good place to start na to ask two heavy questions.

First: Wetin go happen if we intervene?

If government build road, change tax, or expand school program, the intervention go get which specific effect?

Second: Wetin for don happen if e different—if we no do the intervention?

This second question hard. We fit study the world we dey inside, but we no fit directly study the alternate world where the intervention never happen. Still, that alternate world na wetin policymakers for Africa often need sabi.

Observation alone no reach.

Make we take example of one practitioner for Africa wey start program to help farmers produce more food, and later harvest increase. This good to hear, but e no mean say the program na the direct cause. Maybe na luck or one-off event. Maybe rain fall well that season. Maybe crop prices rise. Maybe farmers don dey improve productivity before the program start.

To understand the program effect we need ask counterfactual question: How those farmers for do without the program? Dem for do better or worse?

We fit see this logic for studies wey cross sectors inside agriculture. For example, one study do for low-income adults for Ghana during COVID-19. Researchers wan know if easier way to communicate (extra airtime) fit improve well-being during that hard time.

Instead of only looking at link between communication and mental health, dem randomly give some people extra airtime credit and compare mental health of that group with another group wey no get extra airtime.

The only difference between the two groups na the extra airtime. That action follow by big drop for stress levels.

The lesson wider pass that specific action: when we wan know if changing something go change outcomes, observation alone no dey enough.

We need ask wetin happen when we do real intervention. This one change how we dey think about development.

For many years, development talk sometimes focus on predicting how Africa go be in some decades: population growth rate, number of jobs wey go need, how big cities go be, or how climate change fit affect the economy.

Those predictions fit get value. But prediction no be strategy.

If person predict say millions of African youth go enter the job market, the important question no be only whether the prediction go happen. The important question be wetin we fit do now to change the opportunities those youth go meet tomorrow.

That one need to ask different kind of question: Which things we fit change to get the results we want? Wetin we fit change?

Na here the development challenge for Africa turn to opportunity. This continent no be only place to test predictions about the future; na place where the future dey built for real time.

New transport networks fit change where business dey. Digital payment systems fit change how people dey transact. Energy infrastructure fit decide which factories go fit grow. Better farming technology fit change wetin farmers fit produce. Education fit change the skills wey economy get.

Any action wey we take dey bring possibilities and sometimes results wey hard to see beforehand.

So the goal no suppose be to stop predicting. Prediction still get big importance. But make we no dey treat prediction as the final step.

Most times the main question no be "Wetin go happen later?" The main question suppose be: "Wetin we fit do to bring different results?"

This change of view—from predicting the future to understanding how to change am—is one of the important ideas for modern development.

Africa no only need better predictions about the future.

Africa need better ways to build that future.

Kanusho: The views wey the writer express no be the same with TRT Afrika editorial views or policy.