# References

This page lists the papers that the public estimator pages draw from. The
estimator pages link here so the methodological source is visible before the
usage examples.

Use this page to trace a method back to its source paper. It is a bibliography,
not a claim that every source paper has a full numerical replication in the
package.

## Theory Surveys and Lecture Notes

(kang-2026-lecture)=
- Kang, E. H. (2026). "A Lecture Note on Offline RL and IRL: Part II:
  Foundations of Inverse Reinforcement Learning and Dynamic Discrete Choice
  Models." arXiv preprint arXiv:2605.30843.

(rawat-rust-2026)=
- Rawat, P., and Rust, J. (2026). "Dynamic Discrete Choice and Inverse
  Reinforcement Learning: Inferring Preferences and Beliefs from Human
  Behavior." Manuscript.

## Dynamic Discrete Choice

(rust-1987)=
- Rust, J. (1987). "Optimal Replacement of GMC Bus Engines: An Empirical Model
  of Harold Zurcher." *Econometrica*, 55(5), 999-1033.

(rust-1994)=
- Rust, J. (1994). "Structural Estimation of Markov Decision Processes." In
  *Handbook of Econometrics*, Vol. 4, ch. 51, 3081-3143. North-Holland.

(hotz-miller-1993)=
- Hotz, V. J., and Miller, R. A. (1993). "Conditional Choice Probabilities and
  the Estimation of Dynamic Models." *Review of Economic Studies*, 60(3),
  497-529.

(aguirregabiria-mira-2002)=
- Aguirregabiria, V., and Mira, P. (2002). "Swapping the Nested Fixed Point
  Algorithm: A Class of Estimators for Discrete Markov Decision Models."
  *Econometrica*, 70(4), 1519-1543.

(magnac-thesmar-2002)=
- Magnac, T., and Thesmar, D. (2002). "Identifying Dynamic Discrete Decision
  Processes." *Econometrica*, 70(2), 801-816.

(hotz-miller-sanders-smith-1994)=
- Hotz, V. J., Miller, R. A., Sanders, S., and Smith, J. (1994). "A Simulation
  Estimator for Dynamic Models of Discrete Choice." *Review of Economic
  Studies*, 61(2), 265-289.

(kasahara-shimotsu-2008)=
- Kasahara, H., and Shimotsu, K. (2008). "Pseudo-Likelihood Estimation and
  Bootstrap-Based Inference for Structural Discrete Markov Decision Models."
  *Journal of Econometrics*, 146(1), 92-106.

(kasahara-shimotsu-2009)=
- Kasahara, H., and Shimotsu, K. (2009). "Nonparametric Identification of
  Finite Mixture Models of Dynamic Discrete Choices." *Econometrica*, 77(1),
  135-175.

(aguirregabiria-mira-2010)=
- Aguirregabiria, V., and Mira, P. (2010). "Dynamic Discrete Choice Structural
  Models: A Survey." *Journal of Econometrics*, 156(1), 38-67.

(arcidiacono-miller-2011)=
- Arcidiacono, P., and Miller, R. A. (2011). "Conditional Choice Probability
  Estimation of Dynamic Discrete Choice Models With Unobserved
  Heterogeneity." *Econometrica*, 79(6), 1823-1867.

(cameron-miller-2015)=
- Cameron, A. C., and Miller, D. L. (2015). "A Practitioner's Guide to
  Cluster-Robust Inference." *Journal of Human Resources*, 50(2), 317-372.

(su-judd-2012)=
- Su, C.-L., and Judd, K. L. (2012). "Constrained Optimization Approaches to
  Estimation of Structural Models." *Econometrica*, 80(5), 2213-2230.

(iskhakov-2016)=
- Iskhakov, F., Lee, J., Rust, J., Schjerning, B., and Seo, K. (2016).
  "Comment on 'Constrained Optimization Approaches to Estimation of Structural
  Models'." *Econometrica*, 84(1), 365-370.

(shapiro-xu-2005)=
- Shapiro, A., and Xu, H. (2005). "Stochastic Mathematical Programs with
  Equilibrium Constraints, Modeling and Sample Average Approximation." Published
  in *Optimization*, 57(3), 395-418 (2008).

(koiso-otani-2024)=
- Koiso, S., and Otani, S. (2024). "An MPEC Estimator for the Sequential Search
  Model." arXiv:2409.04378.

## Approximate Structural Estimation

(luo-sang-2024)=
- Luo, Y., and Sang, P. (2024). "Efficient Estimation of Structural Models via
  Sieves." Working paper, University of Toronto.

(nguyen-2025)=
- Nguyen, H. (2025). "Neural Networks for Efficient Estimation of
  High-Dimensional Dynamic Discrete Choice Models." Working paper, Georgetown
  University.

(adusumilli-eckardt-2025)=
- Adusumilli, K., and Eckardt, D. (2025). "Temporal-Difference Estimation of
  Dynamic Discrete Choice Models." Working paper.

## Maximum-Entropy and Adversarial IRL

(ziebart-2008)=
- Ziebart, B. D., Maas, A., Bagnell, J. A., and Dey, A. K. (2008). "Maximum
  Entropy Inverse Reinforcement Learning." *Proceedings of the 23rd AAAI
  Conference on Artificial Intelligence*, 1433-1438.

(ziebart-2010)=
- Ziebart, B. D. (2010). *Modeling Purposeful Adaptive Behavior with the
  Principle of Maximum Causal Entropy*. PhD thesis, Carnegie Mellon University.

(wulfmeier-2015)=
- Wulfmeier, M., Ondruska, P., and Posner, I. (2015). "Maximum Entropy Deep
  Inverse Reinforcement Learning." NIPS Deep Reinforcement Learning Workshop.

(fu-2018)=
- Fu, J., Luo, K., and Levine, S. (2018). "Learning Robust Rewards with
  Adversarial Inverse Reinforcement Learning." *International Conference on
  Learning Representations*.

(barnes-2024)=
- Barnes, M., Abueg, M., Lange, O. F., Deeds, M., Trader, J., Molitor, D.,
  Wulfmeier, M., and O'Banion, S. (2024). "Massively Scalable Inverse
  Reinforcement Learning in Google Maps." *International Conference on Learning
  Representations*. arXiv:2305.11290.

(lee-sudhir-wang-2026)=
- Lee, P. S., Sudhir, K., and Wang, T. (2026). "Consumer Engagement with
  Sequential Content: A Content-Aware Dynamic Choice Model." SSRN working
  paper, abstract 6331041.

## Q-Function and Divergence IRL

(ni-2020)=
- Ni, T., Sikchi, H., Wang, Y., Gupta, T., Lee, L., and Eysenbach, B. (2020).
  "f-IRL: Inverse Reinforcement Learning via State Marginal Matching."
  *Proceedings of the 4th Conference on Robot Learning*.

(garg-2021)=
- Garg, D., Chakraborty, S., Cundy, C., Song, J., and Ermon, S. (2021).
  "IQ-Learn: Inverse Soft-Q Learning for Imitation." *Advances in Neural
  Information Processing Systems*.

(kang-2025)=
- Kang, E. H., Yoganarasimhan, H., and Jain, L. (2025). "An Empirical Risk
  Minimization Approach for Offline Inverse RL and Dynamic Discrete Choice
  Model." *arXiv preprint arXiv:2502.14131*.

## Identification and Related Foundations

(kim-2021)=
- Kim, K., Garg, S., Shiragur, K., and Ermon, S. (2021). "Reward
  Identification in Inverse Reinforcement Learning." *International Conference
  on Machine Learning*.

(cao-2021)=
- Cao, H., Cohen, S. B., and Szpruch, L. (2021). "Identifiability in Inverse
  Reinforcement Learning." *Advances in Neural Information Processing Systems*.

(bray-2019)=
- Bray, R. L. "Unnesting the Fixed Point in the Estimation of Dynamic
  Programs." Working paper, SSRN 3307175.

(oguz-bray-2026)=
- Oguz, E., and Bray, R. L. (2026). "Training Neural Networks Embedded in
  Dynamic Discrete Choice Models." Working paper, arXiv 2604.09736.