In both cases, the answers should be clear when looking at the evidence and the mechanisms at play without an ideological bias

Raimondo, S., Benigni, B., & De Domenico, M. (2020). Environmental conditions and human activity nexus. The case of Northern Italy during COVID-19 lockdown. ArXiv:2010.07721 [Physics]. http://arxiv.org/abs/2010.07721

COVID-19 and the Labor Market. (n.d.). IZA – Institute of Labor Economics. Retrieved October 11, 2020, from https://covid-19.iza.org/publications/dp13749/

Sharkey, P., & Wood, G. (2020). The Causal Effect of Social Distancing on the Spread of SARS-CoV-2 [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/hzj7a

Gruijters, Stefan L.K. ‘The Fallacy of Manipulation “Checks” in Psychological Experiments’. Preprint. PsyArXiv, 20 August 2020. https://doi.org/10.31234/osf.io/fkzv5.

Does the COVID-19 Pandemic Improve Global Air Quality? New Cross-​National Evidence on Its Unintended Consequences. COVID-19 and the Labor Market. (n.d.). IZA – Institute of Labor Economics. Retrieved July 29, 2020, from https://covid-19.iza.org/publications/dp13480/

Arpino, B., Bordone, V., & Pasqualini, M. (2020). Are intergenerational relationships responsible for more COVID-19 cases? A cautionary tale of available empirical evidence [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/y8hpr

Shmueli, G. (2010). To Explain or to Predict? Statistical Science, 25(3), 289–310.

Huitfeldt, A. (2016). Is caviar a risk factor for being a millionaire? BMJ, 355. https://doi.org/10.1136/bmj.i6536

Sperrin, M., Martin, G. P., Sisk, R., & Peek, N. (2020). Missing data should be handled differently for prediction than for description or causal explanation. Journal of Clinical Epidemiology, 0(0). https://doi.org/10.1016/j.jclinepi.2020.03.028

Brodeur, A., Cook, N., & Heyes, A. (2020). A Proposed Specification Check for p-Hacking. AEA Papers and Proceedings, 110, 66–69. https://doi.org/10.1257/pandp.20201078

Dsouza, D. D., Quadros, S., Hyderabadwala, Z. J., & Mamun, M. A. (2020). Aggregated COVID-19 suicide incidences in India: Fear of COVID-19 infection is the prominent causative factor [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/7xa4b

Watts, D. J., Beck, E. D., Bienenstock, E. J., Bowers, J., Frank, A., Grubesic, A., Hofman, J., Rohrer, J. M., & Salganik, M. (2018). Explanation, prediction, and causality: Three sides of the same coin? [Preprint]. Open Science Framework. https://doi.org/10.31219/osf.io/u6vz5

Figure 1 here is particularly important.

Set of minimal conditions together define the sufficient cause or complete causal mechanism.

The expression, "without which the disease or event would not have occurred", points out to another important concept here, the notion of counterfactual theory of causation.

Note the criteria:

1. Cause as an event
2. Cause as a condition
3. Cause as characteristic
4. Cause is antecedent
5. Cause is necessary
6. Cause is conditional We will see all of these conditions expanded

Note that we start with the example of the bulb and then expand to generalise this example to larger issues -- in our case, health.

Another important point -- often the last observable event tends to be considered the "cause" of an outcome. One must be careful to find out other possible causes of an outcome.

This is a very important point in this paper. It points to the notions of multifactorial causality -- that is, an outcome will more often than not have more than one cause, and one cause is not usually sufficient to result in the effect.