I am broadly interested in causal inference and experimental design. I am interested in both better understanding causal inference within experimental settings and bringing experimental tools into observational studies. I have projects that focus on causal inference within classical designs such as blocking and matched pairs, and also work in observational settings with more complicated structures, utilizing, for instance, factorial designs to address problems of causal inference.
Preprints
Pashley, N., Libgober, B., & Dasgupta, T. "Analysis and sample-size determination for 2𝐾 audit experiments with binary response and application to identification of effect of racial discrimination on access to justice." arXiv preprint arXiv:2503.10591.
Blackwell, M. & Pashley, N. E. "Bounds on causal effects in 2𝐾 factorial experiments with non-compliance." arXiv preprint arXiv:2407.12114.
Koo, T. and Pashley, N.E. "Causal Inference for Balanced Incomplete Block Designs." arXiv preprint arXiv:2405.19312.
Blackwell, M., Pashley, N. E, and Valentino, D. "Batch Adaptive Designs to Improve Efficiency in Social Science Experiments."
Pashley, N. E., Hunter, K. B., McKeough, K., Rubin, D. B., and Dasgupta, T. "Causal inference from treatment-control studies having an additional factor with unknown assignment mechanism." arXiv preprint arXiv:2202.03533.
Publications
Pashley, N.E., Miratrix, L.W., and Keele, L. "Improving instrumental variable estimators with poststratification." Journal of the Royal Statistical Society Series A: Statistics in Society. 188(3), 765–790, https://doi.org/10.1093/jrsssa/qnae073
Goplerud, M., Imai, K. and Pashley, N. E. (2025) "Estimating Heterogeneous Causal Effects of High-Dimensional Treatments: Application to Conjoint Analysis." Annals of Applied Statistics. 19(2), 866-888, https://doi.org/10.1214/24-AOAS1994
Ravichandran, A., Pashley, N.E., Libgober, B., and Dasgupta, T. (2024). ``Optimal allocation of sample size for randomization-based inference from 2𝐾 factorial designs.'' Journal of Causal Inference, 12(1), 20230046. https://doi.org/10.1515/jci-2023-0046
Blackwell, M., and Pashley, N. E. (2023) "Noncompliance and instrumental variables for 2𝐾 factorial experiments." Journal of the American Statistical Association. 118(542), 1102-1114, DOI: 10.1080/01621459.2021.1978468
Pashley, N.E. and Bind, M.-A.C. (2023) "Causal inference for multiple treatments using fractional factorial designs." Canadian Journal of Statistics. , 51(2): 444-468. https://doi.org/10.1002/cjs.11734
Ramos, R.S., Cooper, R., Dasgupta, T., Pashley, N. E., Wang, C. (2023) "Comparative Efficacy of Superheated Dry Steam Application and Insecticide Spray Against Common Bed Bugs Under Simulated Field Conditions." Journal of Economic Entomology, 116(1), 12–18, https://doi.org/10.1093/jee/toac070
Schochet, P.Z., Pashley, N.E., Miratrix, L.W., and Kautz, T. (2022) "Design-Based Ratio Estimators and Central Limit Theorems for Clustered, Blocked RCTs." Journal of the American Statistical Association, 117(540), 2135-2146, DOI: 10.1080/01621459.2021.1906685
Pashley, N. E. (2022). "Note on the delta method for finite population inference with applications to causal inference." Statistics & Probability Letters, Volume 188, 109540.
Pashley, N. E., and Miratrix, L. W. (2022) "Block what you can, except when you shouldn't." Journal of Educational and Behavioral Statistics. 47(1), 69-100. doi:10.3102/10769986211027240
Pashley, N. E., Basse, G. W., and Miratrix, L. W. (2021). "Conditional As-If Analyses in Randomized Experiments." Journal of Causal Inference, 9(1), 264-284. https://doi.org/10.1515/jci-2021-0012
Pashley, N. E., and Miratrix, L. W. (2021). "Insights on Variance Estimation for Blocked and Matched Pairs Designs." Journal of Educational and Behavioral Statistics, 46(3), 271–296. https://doi.org/10.3102/1076998620946272
Other Writing
Pashley, N. E. (2025). Book review of "A First Course in Causal Inference" by Peng Ding. Observational Studies 11(2), 209-211. https://dx.doi.org/10.1353/obs.2025.a963649
Cai, J., Pashley, N. E., and Zhao, L. (2021). "2021 IMS Membership Survey: Report". IMS Bulletin, 50(5), 6-9.
Pashley, N. E. (2023). "To block or not to block, that is the question" The Miratrix C.A.R.E.S. Lab Blog. https://cares-blog.gse.harvard.edu/post/can-blocking-hurt/
Accompanying software packages
Koo, T. and Pashley, N. E. (2025). IBDInfer: Design-Based Causal Inference Method for Incomplete Block. R package version 0.0.1 https://CRAN.R-project.org/package=IBDInfer
Goplerud, M., Imai, K., and Pashley, N. E. (2025). FactorHet: Estimate Heterogeneous Effects in Factorial Experiments Using Clustering and Sparsity. R package version 1.0.0. https://CRAN.R-project.org/package=FactorHet
Miratrix, L., and Pashley, N. E. (2024). poststratIV: Post-Stratified Estimation of Complier Average Causal Effects. R package version 0.0.0.9000. https://github.com/lmiratrix/poststratIV
Blackwell, M., and Pashley, N. E. (2021). factiv: Instrumental Variables Estimation for 2𝐾 Factorial Experiments. R package version 0.1.0. https://CRAN.R-project.org/package=factiv
Miratrix, L., and Pashley, N. E. (2020). blkvar: ATE and Treatment Variation Estimation for Blocked and Multisite RCTs. R package version 0.0.1.5. Available at https://github.com/lmiratrix/blkvar