Causality Question: Understanding Inputs Before t1

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In summary, the student is struggling to understand the concept of causality in relation to a tutorial question. Specifically, they are unsure about the significance of the variable 't1' and how it relates to the input signal. They are seeking clarification on whether 't1' is an arbitrary time after 't=0' and how this affects the statement that for 't<t1, x(t)=0'. They apologize for the confusion and are grateful for any help.
  • #1
Schniz2
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This is a question i asked my tutor via email... kind of in a hurry to get it answered so i can fully understand causality for an assignment due on monday.i'm having trouble understanding the causality of the attached tutorial question...
if there is an input 'x(t)', for any 't<0' is this input zero? in the tutorial you wrote:
"say t < t1, x(t) = 0
lambda < t1, x(lambda) = 0
"
i'm not sure what you are referring to as 't1'... is this just an arbitrary time after 't = 0'? or is 't1' the origin of the input signal and any input signal before 't1' is zero.

if it is an arbitrary time after t = 0, how are we allowed to say that for 't < t1, x(t) = 0'?Very grateful if anyone is able to help... cheers ;)****Hmm, maybe this should have not been in this area... couldn't decide whether it was a homework question or just a question about the concept of causality :S****
 

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  • #2
t1<x(t)
therefore
x(t)=0
t1 is the origin so this means it arbitrarily after 0
 
  • #3


I can provide some clarification on the concept of causality and understanding inputs before t1.

In science, causality refers to the relationship between cause and effect. In other words, how one event or factor leads to another event or outcome. In the context of your question, we are looking at the input signal (x(t)) and its relationship to time (t).

In the tutorial, the notation 't < t1' means any time before t1. So, if we have an input signal 'x(t)' and we are looking at any time before t1, then the value of x(t) is equal to 0. This is because before t1, there is no input signal present. Similarly, for any time after t1 (t > t1), the value of x(t) will be non-zero.

Now, you also asked about the origin of the input signal and if any input before t1 is zero. This depends on the specific context of the problem. In some cases, the origin of the input signal may be at t1, meaning that any input before t1 is indeed zero. However, in other cases, the origin of the input signal may be at t = 0, and the input signal may only start at t1. In this case, any input before t1 would also be zero. It's important to understand the context of the problem in order to fully understand the inputs before t1.

In conclusion, the notation 't < t1' simply means any time before t1, and in this context, the value of the input signal is zero. I hope this helps you better understand causality and inputs before t1. If you have any further questions, please don't hesitate to reach out. Best of luck with your assignment!
 

What is causality in science?

Causality refers to the relationship between two events where one event, the cause, brings about the other event, the effect. In scientific research, causality is used to understand the relationship between different variables and to determine whether one variable causes changes in another.

Why is understanding inputs before t1 important in causality?

Understanding inputs before t1 (the time of intervention or exposure) is important because it allows for the identification of potential confounding variables. These are variables that may influence the relationship between the cause and effect, and if not accounted for, can lead to incorrect conclusions about causation.

How do scientists determine causality?

Scientists determine causality by using experimental designs that allow for the manipulation of the cause and the observation of the effect. This allows for the elimination of potential confounding variables and strengthens the evidence for causation.

What is the difference between correlation and causation?

Correlation refers to a relationship between two variables where changes in one variable are associated with changes in the other variable. However, correlation does not necessarily imply causation. Causation requires evidence of a direct relationship between the variables, where the cause precedes the effect and other potential explanations are ruled out.

Can causality be proven?

No, causality cannot be proven definitively. Scientific research can only provide evidence for or against causality, but it is always subject to limitations and potential errors. Therefore, causality is always a matter of inference and can never be proven with absolute certainty.

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