Towards Personalized Learning using Counterfactual Inference for Randomized Controlled Trials

Personalized learning considers that the causal effects of a studied learning intervention may differ for the individual student (e.g., maybe girls do better with video hints while boys do better with text hints). To evaluate a learning intervention inside ASSISTments, we run a randomized control trial (RCT) by randomly assigning students into either a control condition or a treatment condition. Making the inference about causal effects of studies interventions is a central problem. Counterfactual inference answers “What if� questions, such as Would this particular student benefit more if the student were given the video hint instead of the text hint when the student cannot solve a problem? . Counterfactual prediction provides a way to estimate the individual treatment effects and helps us to assign the students to a learning intervention which leads to a better learning. A variant of Michael Jordan\u27s Residual Transfer Networks was proposed for the counterfactual inference. The model first uses feed-forward neural networks to learn a balancing representation of students by minimizing the distance between the distributions of the control and the treated populations, and then adopts a residual block to estimate the individual treatment effect. Students in the RCT usually have done a number of problems prior to participating it. Each student has a sequence of actions (performance sequence). We proposed a pipeline to use the performance sequence to improve the performance of counterfactual inference. Since deep learning has achieved a huge amount of success in learning representations from raw logged data, student representations were learned by applying the sequence autoencoder to performance sequences. Then, incorporate these representations into the model for counterfactual inference. Empirical results showed that the representations learned from the sequence autoencoder improved the performance of counterfactual inference

Testing the 'Brain Gain' Hypothesis: MIcro Evidence from Cape Verde

Does emigration really drain human capital accumulation in origin countries? This paper explores a unique household survey purposely designed and conducted to answer this research question. We analyze the case of Cape Verde, a country with allegedly the highest ‘brain drain’ in Africa, despite a marked record of income and human capital growth in recent decades. Our micro data enables us to propose the first explicit test of ‘brain gain’ arguments according to which the prospects of own future migration can positively impact educational attainment. According to our results, a 10pp increase in the probability of own future migration improves the average probability of completing intermediate secondary schooling by 8pp. Our findings are robust to the choice of instruments and econometric model. Overall, we find that there may be substantial human capital gains from lowering migration barriers.

Regression adjustments for estimating the global treatment effect in experiments with interference

Standard estimators of the global average treatment effect can be biased in the presence of interference. This paper proposes regression adjustment estimators for removing bias due to interference in Bernoulli randomized experiments. We use a fitted model to predict the counterfactual outcomes of global control and global treatment. Our work differs from standard regression adjustments in that the adjustment variables are constructed from functions of the treatment assignment vector, and that we allow the researcher to use a collection of any functions correlated with the response, turning the problem of detecting interference into a feature engineering problem. We characterize the distribution of the proposed estimator in a linear model setting and connect the results to the standard theory of regression adjustments under SUTVA. We then propose an estimator that allows for flexible machine learning estimators to be used for fitting a nonlinear interference functional form. We propose conducting statistical inference via bootstrap and resampling methods, which allow us to sidestep the complicated dependences implied by interference and instead rely on empirical covariance structures. Such variance estimation relies on an exogeneity assumption akin to the standard unconfoundedness assumption invoked in observational studies. In simulation experiments, our methods are better at debiasing estimates than existing inverse propensity weighted estimators based on neighborhood exposure modeling. We use our method to reanalyze an experiment concerning weather insurance adoption conducted on a collection of villages in rural China.Comment: 38 pages, 7 figure

Testing the 'Brain Gain' Hypothesis: Micro Evidence from Cape Verde

Does emigration really drain human capital accumulation in origin countries? This paper explores a unique household survey purposely designed and conducted to answer this research question. We analyze the case of Cape Verde, a country with allegedly the highest 'brain drain' in Africa, despite a marked record of income and human capital growth in recent decades. Our micro data enables us to propose the first explicit test of 'brain gain' arguments according to which the prospects of own future migration can positively impact educational attainment. According to our results, a 10pp increase in the probability of own future migration may improve the average probability of completing intermediate secondary schooling by 8pp for individuals who do not migrate before age 16. Strikingly, this same 10pp increase may raise the probability of completing intermediate secondary schooling by 11pp for an individual whose parents were both non migrants when the educational decision was made. Our findings are robust to the choice of instruments and econometric model. Overall, we find that there may be substantial human capital gains from lowering migration barriers.household survey, Cape Verde, brain drain, brain gain, international migration, human capital, effects of emigration in origin countries, sub-Saharan Africa

Inequality of Opportunity in Brazil

This paper proposes a method to decompose earnings inequality into a component due to unequal opportunities and a residual term. Drawing on the distinction between ‘circumstance’ and ‘effort’ variables in John Roemer’s work on equality of opportunity, we associate inequality of opportunities with the inequality attributable to circumstances which lie beyond the control of the individual – such as her family background, her race and the region where she was born. We interpret the decomposition as establishing a lower bound on the contribution of opportunities to earnings inequality. We further decompose the effect of opportunities into a direct effect on earnings and an indirect component which works through the “effort” variables. The decomposition is applied to the distributions of male and female earnings in Brazil, in 1996. While the residual term is large, observed circumstances nevertheless account for around a quarter of the value of the Theil index. Parental education is by far the most important circumstance affecting earnings, dwarfing the effects of race and place of birth.Inequality of opportunity, earnings inequality, intergenerational mobility