Is More Data All You Need? A Causal Exploration

Balke, A., Pearl, J.: Probabilistic evaluation of counterfactual queries. In: AAAI (1994)

1994

Earlier work this paper cites.

LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86

1998

Earlier work this paper cites.

Pearl, J.: Causality (2nd edition). Cambridge University Press (2009)

2009

Earlier work this paper cites.

Figueroa, R.L., Zeng-Treitler, Q., Kandula, S., Ngo, L.H.: Predicting sample size required for classification performance. BMC medical informatics and decision making 12

2012

Earlier work this paper cites.

Beleites, C., Neugebauer, U., Bocklitz, T., Krafft, C., Popp, J.: Sample size planning for classification models. Analytica chimica acta 760

2013

Earlier work this paper cites.

Cho, J., Lee, K., Shin, E., Choy, G., Do, S.: How much data is needed to train a medical image deep learning system to achieve necessary high accuracy? arXiv preprint arXiv:1511.06348 (2015)

Original

2015

Earlier work this paper cites.

Imbens, G.W., Rubin, D.B.: Causal inference in statistics, social, and biomedical sciences. Cambridge University Press (2015)

2015

Earlier work this paper cites.

Johansson, F., Shalit, U., Sontag, D.: Learning representations for counterfactual inference. In: International Conference on Machine Learning. pp. 3020–3029 (2016)

2016

Earlier work this paper cites.

Shalit, U., Johansson, F.D., Sontag, D.: Estimating individual treatment effect: generalization bounds and algorithms. arXiv preprint arXiv:1606.03976 (2016)

Original

2016

Earlier work this paper cites.

Alaa, A.M., van der Schaar, M.: Bayesian inference of individualized treatment effects using multi-task gaussian processes. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. pp. 3427–3435 (2017)

2017

Earlier work this paper cites.

Alaa, A.M., Weisz, M., Van Der Schaar, M.: Deep counterfactual networks with propensity-dropout. arXiv preprint arXiv:1706.05966 (2017)

Original

2017

Earlier work this paper cites.

Louizos, C., Shalit, U., Mooij, J., Sontag, D., Zemel, R., Welling, M.: Causal effect inference with deep latent-variable models. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. pp. 6449–6459 (2017)

2017

Earlier work this paper cites.

Shalit, U., Johansson, F.D., Sontag, D.: Estimating individual treatment effect: generalization bounds and algorithms. International Conference on Machine Learning (2017)

2017

Cited alongside, same era.

Voigt, P., Von dem Bussche, A.: The eu general data protection regulation (gdpr). A Practical Guide, 1st Ed., Cham: Springer International Publishing 10

2017

Cited alongside, same era.

Kocaoglu, M., Snyder, C., Dimakis, A.G., Vishwanath, S.: Causalgan: Learning causal implicit generative models with adversarial training. In: International Conference on Learning Representations (2018)

2018

Cited alongside, same era.

Schwab, P., Linhardt, L., Karlen, W.: Perfect match: A simple method for learning representations for counterfactual inference with neural networks. arXiv preprint arXiv:1810.00656 (2018)

Original

2018

Cited alongside, same era.

Dhar, P.: The carbon impact of artificial intelligence. Nature Machine Intelligence 2

2020

Later among the works it cites.

Farquhar, S., Gal, Y., Rainforth, T.: On statistical bias in active learning: How and when to fix it. In: International Conference on Learning Representations (2020)

2020

Later among the works it cites.

Pawlowski, N., Castro, D.C., Glocker, B.: Deep structural causal models for tractable counterfactual inference. arXiv preprint arXiv:2006.06485 (2020)

Original

2020

Later among the works it cites.

Richens, J.G., Lee, C.M., Johri, S.: Improving the accuracy of medical diagnosis with causal machine learning. Nature communications 11

2020

Later among the works it cites.

Witty, S., Takatsu, K., Jensen, D., Mansinghka, V.: Causal inference using gaussian processes with structured latent confounders. In: International Conference on Machine Learning. pp. 10313–10323. PMLR (2020)