[[5]{.chapter-number}  [Conclusion and Summary]{.chapter-title}]{#sec-conclusion .quarto-section-identifier}

5 Conclusion and Summary

In conclusion, propensity score methods are useful causal inference tools when working with observational data. While logistic regression is commonly used, machine learning approaches can improve the calibration of propensity scores and lead to better covariate balance. Thus, a better estimate of the treatment effect can be obtained. Particularly, gradient boosting machines perform well with strong theoretical properties in the context probability prediction.

The impact of fair trade certification for coffee producers in developing countries is an agricultural economics problem within a causal inference lense. Replicating Jena et al. (2012) with machine learning propensity scores results in a $10$ fold increase in the estimate treatment effect of certification on per capita income which is a notable finding. The significant change in the covariate balance under a machine learning propensity score is a testament to the value of machine learning for propensity scores in this observational situation.

Although powerful, methods such as inverse propensity weighting assumes that the treatment effect is constant across all individuals and subgroups. Moving forward, a critical area of research at the intersection of machine learning and causal inference is the exploration of treatment effect heterogeneity, which refers to the variation in treatment effects across different individuals or subgroups. This concept is particularly relevant in fields like targeted medicine and policy, where understanding how different groups respond to a treatment or policy can lead to more effective and equitable outcomes.

Existing methods for estimating heterogeneous treatment effects encompass various approaches, including causal trees, causal forests, and metalearners. Causal trees are an adaptation of decision trees, specifically designed to estimate treatment effects by partitioning data into heterogeneous subgroups that exhibit different responses to a treatment. Building on this, causal forests are an implementation of a generalized random forest, improving the stability and estimation of heterogeneous treatment effects. Metalearners, such as T-learners and S-learners, represent another powerful framework, wherein existing machine learning algorithms estimate treatment effects at the individual level.

A particularly promising area of future research lies in enhancing the interpretability of these machine learning algorithms, making them more accessible for real-world decision-making. For example, causal rule ensembles combine the predictive power of machine learning with the transparency of rule-based models. This interpretability is crucial for applying these advanced methods in practical settings, where understanding the rationale behind treatment effects can inform policy and individual decisions.

# Conclusion and Summary {#sec-conclusion} ```{r} #| include: false load(file = "globals.RData") ``` In conclusion, propensity score methods are useful causal inference tools when working with observational data. While logistic regression is commonly used, machine learning approaches can improve the calibration of propensity scores and lead to better covariate balance. Thus, a better estimate of the treatment effect can be obtained. Particularly, gradient boosting machines perform well with strong theoretical properties in the context probability prediction. The impact of fair trade certification for coffee producers in developing countries is an agricultural economics problem within a causal inference lense. Replicating @Jena2012 with machine learning propensity scores results in a $10$ fold increase in the estimate treatment effect of certification on per capita income which is a notable finding. The significant change in the covariate balance under a machine learning propensity score is a testament to the value of machine learning for propensity scores in this observational situation. Although powerful, methods such as inverse propensity weighting assumes that the treatment effect is constant across all individuals and subgroups. Moving forward, a critical area of research at the intersection of machine learning and causal inference is the exploration of treatment effect heterogeneity, which refers to the variation in treatment effects across different individuals or subgroups. This concept is particularly relevant in fields like targeted medicine and policy, where understanding how different groups respond to a treatment or policy can lead to more effective and equitable outcomes. Existing methods for estimating heterogeneous treatment effects encompass various approaches, including causal trees, causal forests, and metalearners. Causal trees are an adaptation of decision trees, specifically designed to estimate treatment effects by partitioning data into heterogeneous subgroups that exhibit different responses to a treatment. Building on this, causal forests are an implementation of a generalized random forest, improving the stability and estimation of heterogeneous treatment effects. Metalearners, such as T-learners and S-learners, represent another powerful framework, wherein existing machine learning algorithms estimate treatment effects at the individual level. A particularly promising area of future research lies in enhancing the interpretability of these machine learning algorithms, making them more accessible for real-world decision-making. For example, causal rule ensembles combine the predictive power of machine learning with the transparency of rule-based models. This interpretability is crucial for applying these advanced methods in practical settings, where understanding the rationale behind treatment effects can inform policy and individual decisions. ```{r} #| include: false save.image(file = "globals.RData") ```