Conditional independence problem

In summary, the conversation discusses the concept of conditional independence between events A1 and A2 given event B, and poses the question of whether this conditional independence holds true for A1 and A2 given the complement of B. A counterexample is provided to disprove this statement, using the example of a probability distribution on a set with three elements. The conversation ends with the realization of the counterexample.
  • #1
bl00d1
6
0
Help needed.
Let A1, A2 and B be events with P(B)>0. Events A1 and A2 are said to be conditionally independent given B if P(A1nA2|B)=P(A1|B)P(A2|B).

Prove or disprove the following statement:

Suppose 0<P(B)<1. If events A1 and A2 are conditionally independent then A1 and A2 are also condtionally independent of Bcomplement.
 
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  • #4
Re: conditional independence problem

bl00d said:
where?

The figure with the squares. If you prefer, consider $\Omega=\{a,b,c\}$ and the probability $p$ on $\mathcal{P}(\Omega)$ defined by $p(a)=p(b)=p(c)=1/3$, and choose $A_1=\{a\}$, $A_2=\{a,b\}$ and $B=\{c\}$. Let's see what do you obtain.
 
  • #5
Re: conditional independence problem

Fernando Revilla said:
The figure with the squares. If you prefer, consider $\Omega=\{a,b,c\}$ and the probability $p$ on $\mathcal{P}(\Omega)$ defined by $p(a)=p(b)=p(c)=1/3$, and choose $A_1=\{a\}$, $A_2=\{a,b\}$ and $B=\{c\}$. Let's see what do you obtain.

oh gosh.. it's right in front of me and i didnt see it
 

FAQ: Conditional independence problem

What is the conditional independence problem?

The conditional independence problem is a common issue in statistical analysis where the relationship between two variables becomes unclear when a third variable is introduced. This can make it difficult to accurately assess the true relationship between the two variables of interest.

Why is the conditional independence problem important?

The conditional independence problem is important because it can lead to incorrect conclusions and misleading results in data analysis. It can also affect the accuracy of predictive models and hinder the ability to make accurate predictions.

What are some common techniques for addressing the conditional independence problem?

There are several techniques that can be used to address the conditional independence problem, such as stratification, regression analysis, and propensity score matching. These methods aim to adjust for the influence of the third variable and clarify the relationship between the two variables of interest.

Can the conditional independence problem be completely eliminated?

No, the conditional independence problem cannot be completely eliminated. It is a natural limitation in data analysis, and there will always be some degree of uncertainty when a third variable is introduced. However, by using appropriate techniques, the impact of the third variable can be minimized.

How does the conditional independence problem relate to causality?

The conditional independence problem is closely related to causality, as it can affect the ability to accurately determine cause-and-effect relationships between variables. Addressing this problem is crucial in establishing a causal relationship between variables and making accurate predictions.

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