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UNICEF is hiring a Home-based: Senior advisor consultant. APPLY NOW

Job no: 571733
Contract type: Consultant
Duty Station: Geneva
Level: Consultancy
Location: Switzerland
Categories: Research, Planning, Monitoring and Evaluation

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UNICEF works in some of the world’s toughest places, to reach the world’s most disadvantaged children. To save their lives. To defend their rights. To help them fulfill their potential.

Across 190 countries and territories, we work for every child, everywhere, every day, to build a better world for everyone.

And we never give up.

For every child, innovation.

Purpose of Activity/Assignment

The usual approach to policy design roughly follows this pattern:

  1. Design and implement a policy
  2. Ask, “did the policy work on average?”
  3. Expand or adjust the program based on the answer to 2.

While valuable, this approach has limitations. Chief among them is that it struggles at identifying if a policy works differently for one group or another because it relies on averages. Average population effects compounds cases where a policy works better than average for some people with those where it doesn’t work for a different group of people.

There are ways to tease out these differences. For example, we could ask “did the policy work on average for X?”. However, answering this question in a repeated fashion is expensive and time consuming. With this approach, it is operationally impossible to get a complete mapping of how a policy works for all the different groups of people it targets.

Recent developments in machine learning can enable governments to change this paradigm. The most robust evaluation methods rely on RCTs or other quasi-experimental methods to estimate average treatment effects (ATEs). Machine learning enables policymakers to shift to a model that looks at the heterogenous effects of policies in more detail, allowing them to adjust and target policy according to the needs of specific sub-groups. Rather than answer “what works on average”, governments will answer “what works for whom”. This will ideally lead to a more impactful, adaptive, and cost-efficient mode of policy making.

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Causal machine learning enables this shift by introducing developments in machine learning to the world of policy evaluation. At a high level, causal forests are composed of decision trees which partition datasets into population clusters. These clusters can then be used to predict and compare the effect of policies at the individual level. This helps policymakers estimate individual average treatment effects (IATE) and group average treatment effects (GATE). Unlike ATES, these two measures do capture heterogeneity in the effect of a policy across population distributions.

Building on the above, UNICEF, along with the Government of Kazakhstan, aims to explore the use of causal machine learning (ML) to enhance social protection policies. Causal ML employs AI algorithms, statistical techniques, and big data to understand the causal effects of interventions such as government programmes. Leveraging Kazakhstan’s administrative data platforms, the project aims to enhance policy effectiveness, generate fiscal savings, and increase the impact of government interventions.

As such and considering the complexity and novelty of the causal machine learning field, UNICEF, together with the government of Kazakhstan, have identified the need for an advisor to build their capacities to implement this technology.

Scope of Work

The exercise will produce a minimum viable open-source pipeline (including data ingestion, algorithm, and analysis of inferred treatment effects) to estimate the causal effect of a policy in Kazakhstan. The model will be trained with government official administrative data. Preliminarily, the government has identified a dataset of about 90 socioeconomic family level indicators, that covers most of the Kazakh population, for the exercise.


The expert will advise the data science team within Kazakhstan’s “Ministry of Digital Development, Innovations and Aerospace Industry” to:

a. Assess the quality of its administrative data and select feasible and relevant policy questions to address. UNICEF and the government of Kazakhstan have pre-identified some possible questions, such as: (i) What is the effect of cash transfers on children with disabilities? (ii) What is the effect of cash transfers on the transition of youth from education into the labor market? (iii) What is the effect of cash transfers on education completion?

b. Develop an algorithm, using open-source tools, that uses causal machine learning approaches to answer one or more such questions. The algorithm should estimate heterogeneous treatment effects and output an analysis of these effects to inform decision making and policy recommendations.

c. Create a plan to further improve the government’s capacity to use causal machine learning to complement its already diverse set of policy making tools. This plan will consider both technical (data quality, human resources) and organizational (political buy-in) components.

Guiding questions

The exercise will explore the feasibility to use causal machine learning to answer policy relevant impact evaluation questions. Item “a” above describes a few potential questions, but in general terms the exercise will answer the following evaluation question:

a. What is the effect (on average, by sub-group, and individual) of X policy on Y child-related outcomes? The outcomes Y and the policy X will be defined jointly with UNICEF and the Government of Kazakhstan.

In addition, and considering the capacity building nature of the project, the following questions will also be considered:

a. What is the minimum quality of administrative data needed by countries to effectively implement their own causal machine learning model?
b. To what extent might quality issues lead to biased predictions and hence biased policy?
c. How do causal machine learning models compare to traditional statistical approaches for policy evaluation?
d. What role can causal machine learning play in policy evaluation and design?
e. What kind of child-related policy questions can be addressed using causal machine learning models?
f. What is a replicable and low-cost model for knowledge transfer on causal machine learning to several countries?
g. Would AI policy recommendations be welcomed by policy makers? How can AI be positioned as another tool at their disposal instead of a threat?

Estimated Duration of the Contract

Approximately 30 working days between June 2024 and March 2025.

Consultant’s Work Place and Official Travel

The Consultant will be remote/home-based.

As part of this assignment, some international travels are foreseen. The consultant will arrange her/his travel as and when they take place, and related costs will be reimbursed per UNICEF travel policy.

Travel Clause

  • All UNICEF rules and regulations related to travel of Consultants apply.
  • All travels shall be undertaken only upon the prior written approval by UNICEF.
  • The consultant must be fit to travel, be in a possession of the valid UN BSAFE certificate, obligatory inoculation(s) and have a valid own travel/medical insurance and an immunization/vaccination card.

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Deadline: 13 May 2024 W. Europe Daylight Time


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