statisticsphd

I am broadly interested in causal inference and causal machine learning (e.g. double/debiased ML or meta-learners), and I am trying to decide between applying to statistics PhD programs and economics PhD programs (and specialize in econometric theory). I definitely can apply to the mix of both, but due to the extremely competitive nature of PhD admissions these days, it seems like a better strategy to focus on a single program and cast a wide net. I have done research in both fields and I think my profile is equally good for both programs. I don't have clear post-PhD plans yet, but I am slightly leaning toward industry than staying in academia (something like industry 6 : academia 4). My rough thought process is Economics PhD Pros: Excellent training in causal inference, get to/expected to work with interesting yet messy observational data in economics, better academic job market Cons: mandatory first-year coursework in micro and macroeconomics and second-year field courses, seem to be obsessed with classical/old/outdated methodologies, will take a year longer potentially (median 6 years to complete), more stressful and longer publication process, worse culture (hierarchy, bureaucracy ...), might not be able to focus on computational side of research (where all the novel and interesting works with actual practical value are happening these days), expected to submit papers to journals rather than (ML) conferences, might not be perceived as good/smart as a statistician in the industry job market. I think statistics PhD has roughly the opposite pros/cons to economics PhD. What do you think?

Any comment or feedback is greatly appreciated!!!

Undergrad Institution: Top 10 Liberal Arts College Major: Mathematics and Statistics GPA: 3.89/4.00 Type of Student: International Male Coursework (all A's otherwise noted): multivariable calculus, proof-based linear algebra 1 (A-) and 2, diff eq, real analysis, abstract algebra, signal processing, numerical analysis, measure theory (B), statistical modeling, calc-based probability, mathematical statistics, measure-theoretic probability, empirical process theory, nonparametric statistics Research Experience: - A 4-month RA at a policy institute, report published at the national policy bulletin. - A summer RA at a well-known business school in my home country, joint appointment at Wharton, the paper is under review at a top business journal with my name mentioned in the acknowledgements. - A summer REU at my home institution in signal processing and medical imaging where I developed a new algorithm, proved the convergence, and tested empirically via simulations. - Ongoing (1.5 years) research in high-dimensional estimation and causal inference. Research project was initially proposed by a faculty mentor, but I have been taking the lead most of the times. Coauthoring a paper. - Ongoing (4 months) research in permutation tests and nonparametric statistics, coauthoring a paper with a faculty mentor. LORs: - A math professor who advised a summer REU, it will be decent. - A stats professor who is currently advising the permutation/nonparametrics research, he said he will write the best letter for me. - An econ professor who is currently coauthoring a paper in high-dimensional estimation/causal inference, it will be great. All the letter writers strongly believe my potential as a researcher. Pertinent Activities or Jobs: - TA for ML class. - TA for probability class. - Two years military service. Concerns: - I'm an international student, so I guess I will be competing against international students coming from international institutions who have stronger math/stats background than me. I don't think my profile is competitive enough considering I will be in the international applicant pool. - B in measure theory (yikes!), nothing much I can do now. Do you think I have a shot at top 10 statistics and top 3 biostatistics PhD programs in the US given this profile? Thanks for your time!