I would like to give you a philosophical and a scientific answer:

In theory and in principle casualty cannot be observed. It never has and never will. Let's take a simple example: when you hit the buttons of your keyboard and the letters appear on your screening typing a post on this website you assume a casual effect. Firstly because you observe correlation between you hitting the keys and letters appearing your screen. And secondly because you have a model of causality of what is happening in your mind which you find plausible (which is basically that the keyboard is an input device used to type).

However, neither of the two are casualty and you cannot observe causality. It could be that an invisible demon creates the letters on your screen every time you hit the keys. That is the philosophical point of view and answer.

The scientific answer is to observe causality: you need to manipulate your input data, control for everything else and observe the effect. Since you're not a psychologist designing a study but analyzing data that means you need to have data over time.

So for example if your assumption is that living in a populated city increases the risk of suffering from clinical depression: then you will need a sample of people living in a big city who later developed clinical depression. And not just a positive correlation between the variable "does live in a big city" and "suffers from clinical depression". And you will also need to control for other independent variables.

Another way to achieve this would be in a labrotary setting where you can explicitly manipulate variables (and it is much easier to control for other independent variables). This approach however is not so much related to data science.

I would like to give you a philosophical and a scientific answer:

In theory and in principle, causality cannot be observed. It never has and never will. Let's take a simple example: when you hit the buttons of your keyboard and the letters appear on your screen whilst typing a post on this website, you assume a causal effect. Firstly, because you observe correlation between you hitting the keys and letters appearing your screen. And secondly, because you have a model of causality of what is happening in your mind which you find plausible (which is basically that the keyboard is an input device used to type).

However, neither of the two are causality and you cannot observe causality. It could be that an invisible demon creates the letters on your screen every time you hit the keys. That is the philosophical point of view and answer.

The scientific answer is to observe causality: you need to manipulate your input data, control for everything else and observe the effect. Since you're not a psychologist designing a study but analyzing data that means you need to have data over time.

So for example if your assumption is that living in a populated city increases the risk of suffering from clinical depression: then you will need a sample of people living in a big city who later developed clinical depression. And not just a positive correlation between the variable "does live in a big city" and "suffers from clinical depression". And you will also need to control for other independent variables.

Another way to achieve this would be in a laboratory setting where you can explicitly manipulate variables (and it is much easier to control for other independent variables). This approach however is not so much related to data science.