Causal inference remains a paramount challenge in contemporary program evaluation, both in the private and public sectors. Researchers strive to establish causality when assessing the impacts of treatments or policy interventions. In recent decades, significant advancements in statistical methods have substantially enhanced our ability to draw causal inferences, thereby mitigating potential biases in treatment effect estimates. These innovative methods, particularly those applicable to observational data (the most common data type in practice),have made a substantial contribution to the field. We specialize in assisting with cutting-edge techniques like balancing methods, including the widely popular Synthetic Control methods. Additionally, we offer expertise in instrumental variables techniques and Difference-in-Differences methods for rigorous causal inference in your program evaluations.

We provide complimentary consultations on these techniques, subject to our current workload. Please feel free to submit a consultation request via the "Contact Us" link in the upper right corner of this page.