Recent advancements in causal inference and machine learning research have brought forward methods to estimate effects of interventions from observational data. The augmented inverse probability weighted (AIPW) estimator is such a method, which can be used to obtain estimates of
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Recent advancements in causal inference and machine learning research have brought forward methods to estimate effects of interventions from observational data. The augmented inverse probability weighted (AIPW) estimator is such a method, which can be used to obtain estimates of potential outcomes. Potential outcomes are defined as a hypothetical outcome pair {Y^{(1)},Y^{(0)}}, of which only one outcome is observed in the data. Estimation of intervention effects boils down to effectively estimating these potential outcomes.

Using the AIPW estimator, we aim to evaluate the average effect of increasing the energy efficiency of houses in the Netherlands on their expected transaction price. Moreover, we investigate how this expected effect changes when we condition on a certain subset of the data.

Given that our assumptions hold, we find that on average, the estimated expected increase in transaction price is positive when improving the energy efficiency of a house. Improving an energy inefficient house to moderately energy efficient is expected to increase the transaction price by approximately €97.70,- per m^{2}, while the improvement from moderately energy efficient to energy efficient increases the expected transaction price by approximately €20.96 per m^{2}. In general, older, smaller and more energy inefficient houses increase most in expected transaction price per m^{2 }when their energy efficiency is improved.