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We analyze which forces explain inflation and how in a large panel of 124 countries from 1997 to 2015. Models motivated by economic theory are compared to an approach based on model-based boosting and non-linearities are explicitly considered. We provide compelling evidence that the interaction of energy price and energy rents stand out among 40 explanatory variables. The output gap and globalization are also relevant drivers of inflation. Credit and money growth, a country's inflation history and demographic changes are comparably less important while central bank related variables as well as political variables turn out to have the least empirical relevance. In a subset of countries public debt denomination and exchange rate arrangements also play a noteworthy role in the inflation process. By contrast, other public-debt variables and an inflation targeting regime have weaker explanatory power. Finally, there is clear evidence of structural breaks in the effects since the financial crisis.

The notion that an independent central bank reduces a country’s inflation is a controversial hypothesis. To date, it has not been possible to satisfactorily answer this question because the complex macroeconomic structure that gives rise to the data has not been adequately incorporated into statistical analyses. We develop a causal model that summarizes the economic process of inflation. Based on this causal model and recent data, we discuss and identify the assumptions under which the effect of central bank independence on inflation can be identified and estimated. Given these and alternative assumptions, we estimate this effect using modern doubly robust effect estimators, i.e., longitudinal targeted maximum likelihood estimators. The estimation procedure incorporates machine learning algorithms and is tailored to address the challenges associated with complex longitudinal macroeconomic data. We do not find strong support for the hypothesis that having an independent central bank for a long period of time necessarily lowers inflation. Simulation studies evaluate the sensitivity of the proposed methods in complex settings when certain assumptions are violated and highlight the importance of working with appropriate learning algorithms for estimation.