causal_ml

Compares three causal machine learning methods: propensity score matching with machine learning, double machine learning, and causal forest across various treatment effect, model complexities, data dimensions and sample sizes.

ML_causal_code_simulation.R

Simulates different data structures based on DGP. The baseline DGP is structured as following:

$y_i=\theta d_i+x_i' \beta + u_i$

$d_i=x_i' \beta + v_i$

The treatment effect $\theta$ is set to $\theta=1$. The $d_i$ represents the binary treatment variable (approximately 50% of the observations receive treatment); $x_i$ represents a vector of k covariates, generated from a multivariate normal distribution; $\beta$ is a vector of k parameters.

The baseline DGP is defined as $n=150$, $k=10$, $\theta=1$ (homogenous treatment). We can then systematically vary these parameters to progressively more challenging estimation problems. In particular, we vary the following parameters, while keeping the others constant:

• Increase sample size $(n=150, 500, 5000, 15000)$
• Increase number of covariates $(k=10, 100)$
• Impose treatment heterogeneity $(\theta=1; \theta \tilde Normal(1,1))$
• Change structure of data $(y_i = \theta d_i + x_i' \beta + u_i)$ and $(y_i = \theta d_i + sin(x_i' \beta) + u_i)$

Runs the following causal estimation methods:

• PSM with Probit regression (PSM-Probit)
• PSM with Neural Net (PSM-NN)
• PSM with Random Forest (PSM-RF)
• PSM with eXtreme Gradient Boosting (PSM-XGB)
• Double machine learning with (E[̂D|Z]) ̂estimated using Logistic Lasso and (E[Y|Z]) ̂estimated using Lasso regression (DML-Lasso/Lasso)
• Double machine learning with (E[̂D|Z]) ̂estimated using logistic regression and (E[Y|Z]) ̂estimated using random forest (DML-Logit/RF)
• Double machine learning with (E[̂D|Z]) ̂estimated using classification neural network and (E[Y|Z]) ̂estimated using Random Forest (DML-NN/RF)
• Double machine learning with (E[̂D|Z]) ̂estimated using Logistic Lasso and (E[Y|Z]) ̂estimated using Random Forest (DML-Lasso/RF)
• Double machine learning with both (E[̂D|Z]) ̂and (E[Y|Z]) ̂estimated using random forest (DML-RF/RF)
• Double machine learning with (E[̂D|Z]) ̂estimated using classification neural network and (E[Y|Z]) ̂estimated using Lasso regression (DML-NN/Lasso)
• Double machine learning with both (E[̂D|Z]) ̂and (E[Y|Z]) ̂estimated using eXtreme Gradient Boosting (DML-XGB/XGB)
• Double machine learning with (E[̂D|Z]) ̂estimated using Logistic regression and (E[Y|Z]) ̂estimated using OLS (DML-Logit/OLS)
• Causal forest (CF)

ML_causal_code_Dhurandhar.R

Causal ML replication of Dhurandhar et al. (2014)

Dhurandhar, E. J., Dawson, J., Alcorn, A., Larsen, L. H., Thomas, E. A., Cardel, M., ... & Allison, D. B. (2014). The effectiveness of breakfast recommendations on weight loss: a randomized controlled trial. The American Journal of Clinical Nutrition, 100(2), 507-513.

ML_causal_code_Bryan.R

Causal ML replication of Bryan et al. (2014)

Bryan, G., Chowdhury, S., & Mobarak, A. M. (2014). Underinvestment in a profitable technology: The case of seasonal migration in Bangladesh. Econometrica, 82(5), 1671-1748.

36174-0001-Data.rda

Dhurandhar et al. (2014) paper data.

Round2.dta

Bryan et al. (2014) paper data.