important in 14 or more runs.<sup>27</sup> Result-stability around variables of importance is more of an issue in LASSO (Mullainathan and Spiess 2017) than in forests with large number of trees (Strobl et al. 2009). Therefore, we ran LASSO 200 times, and present only those variables that show up as important in more than 140 models to remove the role of random chance. This also allows us to account for multicollinearity.

Second, as mentioned in Section 4.1, teacher covariates matter more for predicting student learning gains in Math, compared to Kiswahili. Also, the important predictors are different across the two subjects. It is not surprising that teacher covariates are more important, and differently important, for Math as compared to Kiswahili. Studies have generally found greater variance in teacher effects on achievement in Math than in English (or reading). The difference is due to the large share of language learning that happens at home versus the mostly classroom-based learning that happens for Math (Jackson, Rockoff, and Staiger 2014, Bau and Das 2020).

Third, students’ baseline score is by far the most important variable<sup>28</sup> for predicting student learning gains, potentially signaling mean reversion.

# _4.2.1 Math_

What are the most important predictors of student learning gains in Math? After controlling for a student’s baseline score, the two most important teacher covariates for predicting student learning gains are: (i) teacher practice of providing written feedback to students on their homework / tests and (ii) teacher belief they can help disadvantaged / struggling students learn.<sup>29</sup> These variables have the strongest importance in CIF and are selected as being important by both CIF and LASSO.

Aside from these top variables, other variables of importance occurring in both CIF and LASSO are as follows. Teachers with training in teaching foundational concepts (Reading, Writing, Counting) have higher student learning gains. In addition to providing written feedback, two other teacher practices are important. First, teachers who ask more open ended questions (the critical thinking construct on _Teach_ ) have lower student learning gains. This is one of the very few counter-intuitive results we see but is consistent with findings from other _Teach_ studies signaling a potential measurement and/or interpretation issue in _Teach_ (Filmer et. al 2020).<sup>30</sup> Second, teachers who review concepts taught at the end of class have higher student learning gains.

27 We run CIF 20 times and LASSO 200 times because CIF, which is a collection of trees, is computationally expensive: 20 model runs for CIF take about 55 minutes to execute while 200 runs of LASSO takes less than 1 minute to execute.

28 We explicitly include baseline test score as a control variable even though it is used to calculate student learning gains. We do this to account for threshold effects in the underlying distribution. We are therefore allowing for the fact that learning gains might be qualitatively different for a student who increases her test score from 20% to 30% as compared to a student who goes from 70% to 80%.

29 This can be interpreted as a proxy of whether teachers consider student learning of disadvantaged or struggling students to be within their locus of control. It may also be interpreted as a variable signaling how much ownership teachers take of the learning of struggling or disadvantaged students. It can also be interpreted as a growth mindset indicator for teachers, given that teachers who believe they can improve the learning of struggling/ disadvantaged students may be seen to be having a growth mindset.

30 For instance, it could be signaling that the practice of asking open-ended questions may produce perverse results if key concepts are not explained well.

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