Causality and time series – when an econometrician goes Bayesian

In this section, we’re going to introduce a new style of thinking about causality.

We’ll start this section with a brief overview of quasi-experimental methods. Next, we’ll take a closer look at one of these methods – the synthetic control estimator. We’ll implement the synthetic control estimator using an open source package, CausalPy, from PyMC Labs and test it on real-life data.

Quasi-experiments

Randomized controlled trials (RCTs) are often considered the “gold standard” for causal inference. One of the challenges regarding RCTs is that we cannot carry them out in certain scenarios.

On the other hand, there’s a broad class of circumstances where we can observe naturally occurring interventions that we cannot control or randomize. Something naturally changes in the world, and we are interested in understanding the impact of such an event on...

Wrapping it up

We covered a lot in this chapter. We started by revisiting the S-Learner and T-Learner models and demonstrated how flexible deep learning architectures can help combine the benefits of both models. We implemented TARNet and SNet and learned how to use the PyTorch-based CATENets library.

Next, we delved into the application of causality in NLP. We used a Transformer-based CausalBert model to compute the average treatment effect of a gender avatar on the probability of getting an upvote in a simulated Reddit-like discussion forum.

Finally, we took a glimpse into the world of econometrics and quasi-experimental data and learned how to implement a Bayesian synthetic control estimator using CausalPy.

In the next chapter, we’ll start our adventure with causal discovery.

See you on the other side!

References

Abadie, A. (2021). Using Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspects. Journal of Economic Literature, 59(2), 391-425.

Abadie, A., & Gardeazabal, J. (2003). The Economic Costs of Conflict: A Case Study of the Basque Country. Public Choice & Political Economy Journal.

Benjamens, S., Banning, L. B. D., van den Berg, T. A. J., & Pol, R. A. (2020). Gender Disparities in Authorships and Citations in Transplantation Research. Transplantation Direct, 6(11), e614.

Bradbury, J., Frostig, R., Hawkins, P., Johnson, M. J., Leary, C., Maclaurin, D., Necula, G., Paszke, A., VanderPlas, J., Wanderman-Milne, S., & Zhang, Q. (2018). JAX: composable transformations of Python+NumPy programs [Computer software]: http://github.com/google/jax

Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems...

Causality and time series – when an econometrician goes Bayesian

In this section, we’re going to introduce a new style of thinking about causality.

We’ll start this section with a brief overview of quasi-experimental methods. Next, we’ll take a closer look at one of these methods – the synthetic control estimator. We’ll implement the synthetic control estimator using an open source package, CausalPy, from PyMC Labs and test it on real-life data.

Quasi-experiments

Randomized controlled trials (RCTs) are often considered the “gold standard” for causal inference. One of the challenges regarding RCTs is that we cannot carry them out in certain scenarios.

On the other hand, there’s a broad class of circumstances where we can observe naturally occurring interventions that we cannot control or randomize. Something naturally changes in the world, and we are interested in understanding the impact of such an event on...

Wrapping it up

We covered a lot in this chapter. We started by revisiting the S-Learner and T-Learner models and demonstrated how flexible deep learning architectures can help combine the benefits of both models. We implemented TARNet and SNet and learned how to use the PyTorch-based CATENets library.

Next, we delved into the application of causality in NLP. We used a Transformer-based CausalBert model to compute the average treatment effect of a gender avatar on the probability of getting an upvote in a simulated Reddit-like discussion forum.

Finally, we took a glimpse into the world of econometrics and quasi-experimental data and learned how to implement a Bayesian synthetic control estimator using CausalPy.

In the next chapter, we’ll start our adventure with causal discovery.

See you on the other side!

References

Abadie, A. (2021). Using Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspects. Journal of Economic Literature, 59(2), 391-425.

Abadie, A., & Gardeazabal, J. (2003). The Economic Costs of Conflict: A Case Study of the Basque Country. Public Choice & Political Economy Journal.

Benjamens, S., Banning, L. B. D., van den Berg, T. A. J., & Pol, R. A. (2020). Gender Disparities in Authorships and Citations in Transplantation Research. Transplantation Direct, 6(11), e614.

Bradbury, J., Frostig, R., Hawkins, P., Johnson, M. J., Leary, C., Maclaurin, D., Necula, G., Paszke, A., VanderPlas, J., Wanderman-Milne, S., & Zhang, Q. (2018). JAX: composable transformations of Python+NumPy programs [Computer software]: http://github.com/google/jax

Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems...