Independent assessment of improvements in dementia care and support since 2009

The Department of Health commissioned a team from the London School of Economics and Political Science and the London School of Hygiene and Tropical Medicine to consider progress in dementia care since 2009. We were asked to focus particularly on three areas: improvements in diagnosis and post-diagnostic support, changes in public attitudes, and developments in research. Two major policy documents provide the context: the National Dementia Strategy 2009, which is now finished, and the Prime Minister’s Challenge on Dementia 2012, which superseded it

The Microsegmentation of the Autism Spectrum: economic and research implications for Scotland

Economic research on autism and implications for Scotland, including how the economic cost of autism can inform strategy and planning

How do applied researchers use the Causal Forest? A methodological review of a method

This paper conducts a methodological review of papers using the causal forest machine learning method for flexibly estimating heterogeneous treatment effects. It examines 133 peer-reviewed papers. It shows that the emerging best practice relies heavily on the approach and tools created by the original authors of the causal forest such as their grf package and the approaches given by them in examples. Generally researchers use the causal forest on a relatively low-dimensional dataset relying on randomisation or observed controls to identify effects. There are several common ways to then communicate results -- by mapping out the univariate distribution of individual-level treatment effect estimates, displaying variable importance results for the forest and graphing the distribution of treatment effects across covariates that are important either for theoretical reasons or because they have high variable importance. Some deviations from this common practice are interesting and deserve further development and use. Others are unnecessary or even harmful.Comment: 20 pages, 3 figure

Policy learning for many outcomes of interest: Combining optimal policy trees with multi-objective Bayesian optimisation

Methods for learning optimal policies use causal machine learning models to create human-interpretable rules for making choices around the allocation of different policy interventions. However, in realistic policy-making contexts, decision-makers often care about trade-offs between outcomes, not just singlemindedly maximising utility for one outcome. This paper proposes an approach termed Multi-Objective Policy Learning (MOPoL) which combines optimal decision trees for policy learning with a multi-objective Bayesian optimisation approach to explore the trade-off between multiple outcomes. It does this by building a Pareto frontier of non-dominated models for different hyperparameter settings. The key here is that a low-cost surrogate function can be an accurate proxy for the very computationally costly optimal tree in terms of expected regret. This surrogate can be fit many times with different hyperparameter values to proxy the performance of the optimal model. The method is applied to a real-world case-study of conditional cash transfers in Morocco where hybrid (partially optimal, partially greedy) policy trees provide good performance as a surrogate for optimal trees while being computationally cheap enough to feasibly fit a Pareto frontier.Comment: 15 pages, 6 figure

Fairness Implications of Heterogeneous Treatment Effect Estimation with Machine Learning Methods in Policy-making

Causal machine learning methods which flexibly generate heterogeneous treatment effect estimates could be very useful tools for governments trying to make and implement policy. However, as the critical artificial intelligence literature has shown, governments must be very careful of unintended consequences when using machine learning models. One way to try and protect against unintended bad outcomes is with AI Fairness methods which seek to create machine learning models where sensitive variables like race or gender do not influence outcomes. In this paper we argue that standard AI Fairness approaches developed for predictive machine learning are not suitable for all causal machine learning applications because causal machine learning generally (at least so far) uses modelling to inform a human who is the ultimate decision-maker while AI Fairness approaches assume a model that is making decisions directly. We define these scenarios as indirect and direct decision-making respectively and suggest that policy-making is best seen as a joint decision where the causal machine learning model usually only has indirect power. We lay out a definition of fairness for this scenario - a model that provides the information a decision-maker needs to accurately make a value judgement about just policy outcomes - and argue that the complexity of causal machine learning models can make this difficult to achieve. The solution here is not traditional AI Fairness adjustments, but careful modelling and awareness of some of the decision-making biases that these methods might encourage which we describe.Comment: 13 pages, 1 figur

Productivity dispersion and sectoral labour shares in Europe. ESRI Working Paper 659 May 2020.

The stability of the labour share of income is a fundamental feature of macroeconomic models, with broad implications for the shape of the production function, inequality, and macroeconomic dynamics. However, empirically, this share has been slowly declining in many countries for several decades, though its causes are subject of much debate. This paper analyses the drivers of labour share developments in Europe at a sectoral level. We begin with a simple shift-share analysis which demonstrates that the decline across countries has been primarily driven by changes within industries. We then use aggregated microdata from CompNet to analyse drivers of sector-level labour shares and to decompose their effects into shifts in the sector average or reallocation of resources between firms. Our main findings are that the advance of globalisation and the widening productivity gap between “the best and the rest” have negative implications for the labour share. We also find that most of the changes are due to reallocation within sectors providing support for the “superstar firms” hypothesis. The finding that globalisation has had a negative impact on the labour share is of relevance for policy in the context of the current backlash against globalisation and reinforces the need to ensure benefits of globalisation and productivity are passed on to workers

The case for investment in technology to manage the global costs of dementia

Worldwide growth in the number of people living with dementia will continue over the coming decades and is already putting pressure on health and care systems, both formal and informal, and on costs, both public and private. One response could be to make greater use of digital and other technologies to try to improve outcomes and contain costs. We were commissioned to examine the economic case for accelerated investment in technology that could, over time, deliver savings on the overall cost of care for people with dementia. Our short study included a rapid review of international evidence on effectiveness and cost-effectiveness of technology, consideration of the conditions for its successful adoption, and liaison with people from industry, government, academic, third sector and other sectors, and people with dementia and carers. We used modelling analyses to examine the economic case, using the UK as context. We then discussed the roles that state investment or action could play, perhaps to accelerate use of technology so as to deliver both wellbeing and economic benefits

Transparency challenges in policy evaluation with causal machine learning -- improving usability and accountability

Causal machine learning tools are beginning to see use in real-world policy evaluation tasks to flexibly estimate treatment effects. One issue with these methods is that the machine learning models used are generally black boxes, i.e., there is no globally interpretable way to understand how a model makes estimates. This is a clear problem in policy evaluation applications, particularly in government, because it is difficult to understand whether such models are functioning in ways that are fair, based on the correct interpretation of evidence and transparent enough to allow for accountability if things go wrong. However, there has been little discussion of transparency problems in the causal machine learning literature and how these might be overcome. This paper explores why transparency issues are a problem for causal machine learning in public policy evaluation applications and considers ways these problems might be addressed through explainable AI tools and by simplifying models in line with interpretable AI principles. It then applies these ideas to a case-study using a causal forest model to estimate conditional average treatment effects for a hypothetical change in the school leaving age in Australia. It shows that existing tools for understanding black-box predictive models are poorly suited to causal machine learning and that simplifying the model to make it interpretable leads to an unacceptable increase in error (in this application). It concludes that new tools are needed to properly understand causal machine learning models and the algorithms that fit them.Comment: 31 pages, 8 figure