Contact Information
Department of Economics
Northwestern University
2211 Campus Drive
Evanston, IL 60208 Phone:
725-219-9755
Email: obradovicfilip@u.northwestern.edu
Website: https://filipobradovic.com/
Education
Ph.D., Economics, Northwestern University, 2025 (expected)
MA, Economics, Northwestern University, 2020
M.Sc., Econometrics/Finance, University of Belgrade 2018/2019
B.Sc., Economics (with honors), University of Belgrade, 2015
Primary Fields of Specialization
Econometrics, Applied Econometrics
Secondary Fields of Specialization
Applied Microeconomics
Curriculum Vitae
Job Market Paper
Identification of Long-Term Treatment Effects via Temporal Links, Observational, and Experimental Data. [Download]
(Data analysis in progress.)
Recent literature proposes combining short-term experimental and long-term observational data to provide credible alternatives to conventional observational studies for identification of long-term average treatment effects (LTEs). I show that experimental data have an auxiliary role in this context. They bring no identifying power without additional modeling assumptions. When modeling assumptions are imposed, experimental data serve to amplify their identifying power. If the assumptions fail, adding experimental data may only yield results that are farther from the truth. Motivated by this, I introduce two assumptions on treatment response that may be defensible based on economic theory or intuition. To utilize them, I develop a novel two-step identification approach that centers on bounding temporal link functions –– the relationship between short-term and mean long-term potential outcomes. The approach provides sharp bounds on LTEs for a general class of assumptions, and allows for imperfect experimental compliance — extending existing results.
Publications
Measuring Diagnostic Test Performance Using Imperfect Reference Tests: A Partial Identification Approach. [Download] Journal of Econometrics 2024.
Diagnostic tests are almost never perfect. Studies quantifying their performance use knowledge of the true health status, measured with a reference diagnostic test. Researchers commonly assume that the reference test is perfect, which is often not the case in practice. When the assumption fails, conventional studies identify “apparent” performance or performance with respect to the reference, but not true performance. This paper provides the smallest possible bounds on the measures of true performance – sensitivity (true positive rate) and specificity (true negative rate), or equivalently false positive and negative rates, in standard settings. Implied bounds on policy-relevant parameters are derived: 1) Prevalence in screened populations; 2) Predictive values. Methods for inference based on moment inequalities are used to construct uniformly consistent confidence sets in level over a relevant family of data distributions. Emergency Use Authorization (EUA) and independent study data for the BinaxNOW COVID-19 antigen test demonstrate that the bounds can be very informative. Analysis reveals that the estimated false negative rates for symptomatic and asymptomatic patients are up to 3.17 and 4.59 times higher than the frequently cited “apparent” false negative rate. Further applicability of the results in the context of imperfect proxies such as survey responses and imputed protected classes is indicated.
Working Papers
Identification and Inference on Treatment Effects under Covariate-Adaptive Randomization and Imperfect Compliance. [Download] (with Federico Bugni, Mengsi Gao, and Amilcar Velez)
Randomized controlled trials (RCTs) frequently utilize covariate-adaptive randomization (CAR) (e.g., stratified block randomization) and commonly suffer from imperfect compliance. This paper studies the identification and inference for the average treatment effect (ATE) and the average treatment effect on the treated (ATT) in such RCTs with a binary treatment. We first develop characterizations of the identified sets for both estimands. Since data are generally not i.i.d. under CAR, these characterizations do not follow from existing results. We then provide consistent estimators of the identified sets and asymptotically valid confidence intervals for the parameters. Our asymptotic analysis leads to concrete practical recommendations regarding how to estimate the treatment assignment probabilities that enter in estimated bounds. In the case of the ATE, using sample analog assignment frequencies is more efficient than using the true assignment probabilities. On the contrary, using the true assignment probabilities is preferable for the ATT.
On the Power Properties of Inference for Parameters with Interval Identified Sets. [Download] (with Federico Bugni, Mengsi Gao, and Amilcar Velez)
This paper studies the power properties of confidence intervals (CIs) for a partially-identified parameter of interest with an interval identified set. We assume the researcher has bounds estimators to construct the CIs proposed by Stoye (2009), referred to as 
Under these conditions, we establish two results. First, we show that
Binary Classifiers as Dilations. [Download] (with Gabriel Ziegler)
Seidenfeld and Wasserman (1993) define the phenomenon of dilation. When a dilation occurs, any additional information only increases the uncertainty about the true state of the world. In this paper, we show that dilation may manifest in real-world scenarios when information is provided by binary classifiers, such as diagnostic tests and predictive algorithms. This can happen when classifier performance measures are partially identified due to an imperfect reference classifier, which is common in practice. We characterize when a dilation occurs and develop corresponding inference procedures based on methods for subvector inference in moment inequality models. We apply the approach to diagnostic procedures for COVID-19 detection, using CT chest scans evaluated by radiologists and AI algorithms. We cannot reject the hypothesis that the radiologists’ assessments exhibit a dilation, thus showcasing a potential real-world instance of a dilation. We additionally illustrate the broader applicability of our methodology by rejecting the hypothesis that data-mining techniques for predicting the riskiness of credit card applications are non-informative in the sense of dilation.
Work in Progress
Using Baseline Data to Guide Experimental Design.
Randomized controlled trials (RCTs) are costly. Designs with optimal treatment assignment probabilities — the Neyman allocation — may provide more powerful inference for a fixed sample size. Work implementing such designs usually relies on pilot data which are rarely available in practice. However, availability of baseline outcome and treatment data is common. I pose the experimental design as a decision problem and show how baseline data may be used to inform it. This yields the minimax and minimax regret optimal assignment probabilities that result in asymptotic variances that are minimax and minimax regret optimal under a large class of assignment mechanisms, including stratified block randomization. I illustrate the utility of the findings using empirically calibrated simulations.
Medical Research
Work done in collaboration with a team of transplant surgeons, hepatologists, and econometricians at the Northwestern University Transplant Outcomes Research Collaborative (NUTORC). Some drafts are currently unavailable for circulation due to publication restrictions.
Comparing the Cost of Cirrhosis to Other Common Chronic Diseases: A Longitudinal Study in A Large National Insurance Database. (First author) Forthcoming in Hepatology.
Hospitalization Rates in a Longitudinal US Cohort of Insured Patients with Cirrhosis. [Download]
Predicting Decompensation Risk of Patients with Cirrhosis. (Work in progress)
Teaching
You may download my teaching evaluations here.
References
Prof. Charles Manski (Co-chair)
Prof. Ivan Canay (Co-chair)
Prof. Federico Bugni
Prof. Daniela Ladner